Systems, methods and computer program products for building a database associating N-grams with cognitive motivation orientations

ABSTRACT

Computer-implemented methods can transform a corpus of meaningful text sequences into a generalized computer-usable repository of neurolinguistic information that can be applied by one or more computer systems. The computer system(s) can use the neurolinguistic information to neurolinguistically analyze meaningful text sequences to derive statistical information and identify dominant cognitive motivation orientations expressed in those text sequences. The identified dominant cognitive motivation orientations can be used to improve the efficacy of both human-generated and machine-generated communications. The computer system(s) thereby transform a meaningful text sequence into actionable information about the dominant cognitive motivation orientation(s) of the author of that text sequence within the context in which the text sequence was composed. Computer systems and computer-program products for implementing the methods are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.13/865,996, filed on Apr. 18, 2013, entitled SYSTEMS, METHODS ANDCOMPUTER PROGRAM PRODUCTS FOR BUILDING A DATABASE ASSOCIATING N-GRAMSWITH COGNITIVE MOTIVATION ORIENTATIONS, which claims priority toProvisional U.S. Application No. 61/677,074 filed on Jul. 30, 2012, theentirety of which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to analysis of text sequences, and moreparticularly to computer-implemented neurolinguistic analysis of textsequences to discern information about cognitive motivation orientationsexpressed therein.

BACKGROUND

Written communication among people provides many opportunities formisunderstandings, conflicts and lost opportunities because of the lackof non-textual visual, tonal and often contextual clues. One primaryreason for this is that written communication is typically betweenparties who are physically remote from one another, and therefore do nothave the benefit of hearing vocal intonations or seeing one another'sbody language during the communication. Without these non-textual clues,the communicating parties must attempt to interpret one another basedonly upon the text as it appears, often complete with typographicalerrors, ambiguity and vagueness. These problems can be compounded by alack of skill in writing.

Although traditional paper letters sent by mail or fax usually receive areasonable degree of care and attention from their authors, today manyimportant written messages use instantaneous modes of communicationwhich encourage rapid, “spur of the moment” correspondence. Examples ofsuch types of communication include electronic mail (e-mail), textmessaging via Short Message Service (SMS) or other systems, as well asthrough various network based media on the Internet or otherwise,including online chat systems and social media websites such asFacebook, LinkedIn and Twitter, among others. Such communications aretypically composed quickly, with little planning or attention to detail,and are often written in an informal style.

Not surprisingly, a recipient of such written correspondence maymisunderstand the meaning or intention of the author, which can requireadditional written correspondence or telephone conversations to resolve.In some cases, relationships can be damaged where a recipient perceivesan insult where none was intended. Even where this is not the case, thewrong choice of words in responding to correspondence may fail tomotivate the sender to take the action that the responder wants. Coupledwith the fact that many people receive dozens or even hundreds of e-mailand text messages a day, there is enormous potential formiscommunication and missed opportunity.

Beginning in the 1970s, a field known as “neurolinguistics” began todevelop. According to neurolinguistic theory, people do not choose thewords they use accidentally; the language a person uses is an indicationof how they are thinking One development in neurolinguistics was theidentification of “meta programs” describing how a person thinks, getsmotivated and makes decisions. Initially, approximately 60 meta programswere identified; Rodger Bailey divided a subset of these meta programsinto motivation traits and working traits. Motivation traits are thepatterns that indicate what a person needs to get motivated and staymotivated, and working traits describe the internal mental processingthat a person uses in a particular situation. Further researchdetermined that meta programs were not a fixed representation of aperson's personality, cognition or psychology, but instead shifted basedon the context. The meta programs might be quite different if a personwere at work, doing grocery shopping, worrying about a particularproblem or lying on a beach. It was also determined that the language aperson used in communication is an indicator of the operative metaprograms at the time of that communication.

Thus, a meta program is an example of what is referred to herein as a“cognitive motivation orientation”, which refers to factors, patternsand/or elements that describe how a person thinks, becomes motivated andmakes decisions in a given context, as determined from the language usedin that context. One or more cognitive motivation orientations may beexpressed within a given communication, and these reflect how the authorthinks, becomes motivated and makes decisions in the context in whichthe communication originated. A cognitive motivation orientation istherefore different from a personality profile or psychological profilein that a cognitive motivation orientation relates to a particularcontext, whereas a personality profile or psychological profile attemptsto provide an overall characterization of an individual.

Importantly, it has also been determined that how people react tocertain types of language is related to the operative cognitivemotivation orientation. Knowledge of a person's operative cognitivemotivation orientation could therefore be used to predict and influencethat person's behavior. Using language appropriate to the operativecognitive motivation orientation can create a positive impact whileusing incorrect language can easily trigger a negative reaction. Adetailed discussion is provided in the book “Words That ChangeMinds—Mastering the Language of Influence” by Shelle Rose Charvet, aco-inventor hereof, published by Kendall/Hunt Publishing Company, 4050Westmark Drive, Dubuque, Iowa 52002, Library of Congress Catalogue CardNumber 97-70788, ISBN 978-0-7872-3479-9, the teachings of which arehereby incorporated by reference (all rights reserved).

A trained individual can, through judicious observation of a person'suse of language, determine which cognitive motivation orientations areoperative for the person in that context. It also takes training to beable to choose the correct words, phrases and actions to influencepeople based on their operative cognitive motivation orientations. Thesecognitive motivation orientations operate largely outside of a person'snormal awareness, as few people think about how they are thinking frommoment to moment. Even with training to identify the specific indicatorsin language structures or behaviors, one's ability to objectively,correctly identify the cognitive motivation orientations can easily becolored by one's own unconscious preferences or dislikes. People oftenuse trial and error strategies, which take time, and in high-speedday-to-day communication, this means opportunities to have a positiveimpact can easily be lost. Moreover, a human being, even ahighly-trained one, would not be able to identify cognitive motivationorientations quickly, objectively and consistently because of inherentbiases.

SUMMARY

An automated tool can objectively identify the cognitive motivationorientations expressed in a meaningful text sequence, and can use thisinformation in order to improve the effectiveness of written and oralcommunication, whether human-generated or machine-generated. Applicationof statistical methods to documents that have been annotated by a humanexpert can be used to build an analysis database that can be used by theautomated tool.

A computer-implemented method for neurolinguistically analyzing textcomprises receiving a meaningful first text sequence, deriving firsttext sequence n-grams from the first text sequence, and comparing thefirst text sequence n-grams to a plurality of predetermined indicatorn-grams. Each predetermined indicator n-gram is associated with at leastone cognitive motivation orientation, and each predetermined indicatorn-gram has, for each cognitive motivation orientation with which it isassociated, a corresponding cognitive motivation orientation confidenceweight. For each first text sequence n-gram matching a predeterminedindicator n-gram, the method records the corresponding cognitivemotivation orientation confidence weight for each cognitive motivationorientation associated with that predetermined indicator n-gram.

The method may further comprise using the recorded cognitive motivationorientation confidence weights for the first text sequence to determinea first dominant cognitive motivation orientation set expressed in thefirst text sequence.

In one embodiment, recording the corresponding cognitive motivationorientation confidence weight for each cognitive motivation orientationassociated with a particular predetermined indicator n-gram comprisesincrementing a corresponding first text sequence cognitive motivationorientation weight score for each cognitive motivation orientationassociated with that predetermined indicator n-gram according to thecorresponding cognitive motivation orientation confidence weight. Insuch an embodiment, the method may further comprise normalizing thefirst text sequence cognitive motivation orientation weight scores toobtain normalized first text sequence cognitive motivation orientationweight scores. The method may yet further comprise comparing thenormalized first text sequence cognitive motivation orientation weightscores to respective corresponding normalized dominance thresholds andranking the normalized first text sequence cognitive motivationorientation weight scores according to the difference between eachnormalized first text sequence cognitive motivation orientation weightscore and its respective corresponding normalized dominance threshold toobtain ranked normalized first text sequence cognitive motivationorientation weight scores.

The method may generate a first dominant cognitive motivationorientation set by identifying as dominant for the first text sequenceeach cognitive motivation orientation for which the associatednormalized first text sequence cognitive motivation orientation weightscore exceeds a corresponding normalized dominance threshold, andidentifying as non-dominant for the first text sequence each cognitivemotivation orientation for which the associated normalized first textsequence cognitive motivation orientation weight score does not exceedthe corresponding normalized dominance threshold. The first dominantcognitive motivation orientation set may be generated by comparing thenormalized first text sequence cognitive motivation orientation weightscores to respective corresponding normalized dominance thresholds andranking the normalized first text sequence cognitive motivationorientation weight scores according to the difference between eachnormalized first text sequence cognitive motivation orientation weightscore and its respective corresponding normalized dominance threshold toobtain ranked normalized first text sequence cognitive motivationorientation weight scores. The first dominant cognitive motivationorientation set may comprise m or fewer cognitive motivationorientations, where m is a positive integer, and where m or morecognitive motivation orientations are identified as dominant for thefirst text sequence, the dominant cognitive motivation orientation setmay comprise the m most highly ranked cognitive motivation orientations.

The method may further comprise providing recommendations forpreparation of a meaningful second text sequence addressing the firsttext sequence, with the recommendations being based on the firstdominant cognitive motivation orientation set.

In some embodiments, the method may further comprise receiving ameaningful second text sequence addressing the first text sequence,deriving second text sequence n-grams from the second text sequence,comparing the second text sequence n-grams to the plurality ofpredetermined indicator n-grams, and, for each second text sequencen-gram matching a predetermined indicator n-gram, recording thecorresponding cognitive motivation orientation confidence weight foreach cognitive motivation orientation associated with that predeterminedindicator n-gram. The recorded cognitive motivation orientationconfidence weights for the first text sequence may be used to determinea first dominant cognitive motivation orientation set expressed in thefirst text sequence, and the recorded cognitive motivation orientationconfidence weights for the second text sequence may be used to determinea second dominant cognitive motivation orientation set expressed in thesecond text sequence. The method may further comprise testing whetherthe second dominant cognitive motivation orientation set fits the firstdominant cognitive motivation orientation set. Responsive to adetermination that the second dominant cognitive motivation orientationset fits the first dominant cognitive motivation orientation set, thefit may be confirmed, and responsive to a determination that the seconddominant cognitive motivation orientation set misfits the first dominantcognitive motivation orientation set for the first text sequence, themisfit may be identified. The method may yet further comprise,responsive to identifying the misfit, providing recommendations formodifying the second text sequence to fit a corresponding modifiedsecond dominant cognitive motivation orientation set to the firstdominant cognitive motivation orientation set.

A computer-implemented method for analyzing text comprises receiving ameaningful first text sequence and neurolinguistically analyzing thefirst text sequence to generate first statistical informationrepresenting cognitive motivation orientations expressed within thefirst text sequence.

In one embodiment, neurolinguistically analyzing the first text sequencecomprises extracting elements from the first text sequence andconducting a first comparison by comparing the elements extracted fromthe first text sequence to predetermined elements associated withcognitive motivation orientations to derive the first statisticalinformation, and further comprises determining a first dominantcognitive motivation orientation set expressed within the first textsequence based on the first statistical information.

The method may further comprise providing recommendations forpreparation of a meaningful response addressing the first text sequence,with the recommendations being based on the first dominant cognitivemotivation orientation set. The response may be, for example, ameaningful second text sequence.

The method may further comprise receiving a meaningful second textsequence and neurolinguistically analyzing the second text sequence byextracting elements from the second text sequence and conducting asecond comparison by comparing the elements extracted from the secondtext sequence to predetermined elements associated with cognitivemotivation orientations. Second statistical information is derived fromthe second comparison. A second dominant cognitive motivationorientation set expressed within the second text sequence may bedetermined based on the second statistical information, and the methodmay further comprise testing fit between the first dominant cognitivemotivation orientation set and the second dominant cognitive motivationorientation set. Responsive to a determination that the second dominantcognitive motivation orientation set misfits the first dominantcognitive motivation orientation set expressed, the misfit can beidentified. Also responsive to a determination that the second dominantcognitive motivation orientation set misfits the first dominantcognitive motivation orientation set, the method may comprise presentingrecommendations for modifying the second text sequence to fit acorresponding modified second dominant cognitive motivation orientationset to the first dominant cognitive motivation orientation set.

In one embodiment, the first text sequence is an e-mail message and thesecond text sequence is an unsent e-mail response to the first textsequence; in another embodiment the first text sequence is atranscription of a verbal communication.

In some embodiments, the method may further comprise automaticallyselecting a predetermined response to the first text sequence from a setof predetermined responses based on the first dominant cognitivemotivation orientation set. The predetermined response may be, forexample, an advertisement.

In other embodiments, the method may further comprise automaticallydynamically generating a response to the first text sequence based onthe first dominant cognitive motivation orientation set expressed withinthe first text sequence; the automatically dynamically generatedresponse may be, for example, an advertisement.

The first text sequence may be a search term entered into a searchengine.

A computer-implemented method for receiving an analysis of textcomprises transmitting a meaningful first text sequence from a firstcomputer system and receiving, at the first computer system, acommunication responsive to at least a result of automatedneurolinguistic analysis of cognitive motivation orientations expressedin the first text sequence.

In one embodiment, the communication is an advertisement. In anotherembodiment, the communication comprises first statistical informationrepresenting cognitive motivation orientations expressed within thefirst text sequence. In yet another embodiment, the communicationcomprises an identification of a neurolinguistically-determined firstdominant cognitive motivation orientation set expressed within the firsttext sequence. The method may further comprise providing recommendationsfor preparation of a meaningful second text sequence addressing thefirst text sequence, with the recommendations being based on a firstdominant cognitive motivation orientation set.

The method may further comprise transmitting a meaningful second textsequence from the first computer system, and in such embodiments thecommunication received at the first computer system may be responsive tothe result of the automated neurolinguistic analysis of the cognitivemotivation orientations expressed within the first text sequence andalso to a result of automated neurolinguistic analysis of cognitivemotivation orientations expressed within the second text sequence. Inone particular embodiment, the communication received at the firstcomputer system may comprise first statistical information representingthe cognitive motivation orientations expressed within the first textsequence and second statistical information representing the cognitivemotivation orientations expressed within the second text sequence, andthe method may further comprise using the first statistical informationand the second statistical information to carry out a test of fitbetween a first dominant cognitive motivation orientation set expressedwithin the first text sequence and a second dominant cognitivemotivation orientation set expressed within the second text sequence. Inanother particular embodiment, the communication received at the firstcomputer system may be responsive to the outcome of a test of fitbetween a neurolinguistically-determined first dominant cognitivemotivation orientation set expressed within the first text sequence anda neurolinguistically-determined second dominant cognitive motivationorientation set expressed within the second text sequence.

The communication received at the first computer system may identify atleast one recommendation for modifying the second text sequence to fit acorresponding modified second dominant cognitive motivation orientationset to the first dominant cognitive motivation orientation set.

In some embodiments, the first text sequence is an e-mail message andthe second text sequence is an unsent e-mail response to the first textsequence. In other embodiments, the first text sequence is atranscription of a verbal communication.

In certain embodiments, the communication received at the first computersystem comprises a predetermined response to the first text sequence,with the predetermined response being automatically selected from a setof predetermined responses based on the result of the automatedneurolinguistic analysis of the first text sequence. In certain otherembodiments, the communication received at the first computer systemcomprises an automatically dynamically generated response to the firsttext sequence based on the result of the automated neurolinguisticanalysis of the first text sequence.

The first text sequence may be a search term entered into a searchengine.

A method for building an analysis database associating n-grams withcognitive motivation orientations comprises receiving a training corpusof training documents. Each training document comprises a plurality ofmeaningfully arranged words, and each training document has at least oneannotated word sequence therein. Each annotated word sequence isannotated with a corresponding word-sequence-level annotationidentifying at least one cognitive motivation orientation associatedwith that annotated word sequence. The method further comprisesgenerating indicator candidate n-grams by, for each annotated wordsequence in each training document, extracting n-grams overlapping thatannotated word sequence and associating each extracted n-gram with thecognitive motivation orientation(s) associated with that annotated wordsequence, and applying at least one relevance filter to the indicatorcandidate n-grams to obtain a set of indicator n-grams. Each indicatorn-gram has as its associated cognitive motivation orientation thecognitive motivation orientation with which the corresponding indicatorcandidate n-gram is most frequently associated.

In certain preferred embodiments, applying at least one relevance filterto the indicator candidate n-grams to obtain indicator n-grams comprisesat least one of:

-   -   (a) excluding from the set of indicator n-grams those indicator        candidate n-grams for which a number of times that the        respective indicator candidate n-gram appears in the training        corpus in association with the cognitive motivation orientation        with which the corresponding indicator candidate n-gram is most        frequently associated is less than a predetermined minimum        multiple of a number of times that the respective indicator        candidate n-gram appears in the training corpus in association        with the cognitive motivation orientation with which the        corresponding indicator candidate n-gram is second-most        frequently associated;    -   (b) excluding from the set of indicator n-grams those indicator        candidate n-grams for which the number of times that the        respective indicator candidate n-gram appears in the training        corpus in association with the cognitive motivation orientation        with which the corresponding indicator candidate n-gram is most        frequently associated is less than a predetermined minimum        number; and    -   (c) excluding from the set of indicator n-grams those indicator        candidate n-grams for which a percentage of appearances of the        respective indicator candidate n-gram in the training corpus in        association with the cognitive motivation orientation with which        the corresponding indicator candidate n-gram is most frequently        associated is less than a predetermined minimum percentage of        the total number of appearances of the respective indicator        candidate n-gram in the training corpus.

The method may further comprise assigning a confidence weight to eachindicator n-gram. Assigning a confidence weight to each indicator n-grammay comprises assigning a confidence weight equal to:

-   -   (a) a number of times that the respective indicator candidate        n-gram appears in the training corpus in association with the        cognitive motivation orientation with which the corresponding        indicator candidate n-gram is most frequently associated;        divided by    -   (b) a total number of times that the respective indicator        candidate n-gram appears in the training corpus.

The method may further comprise building a normalized dominancethreshold for the cognitive motivation orientations. In one embodiment,building a normalized dominance threshold for the cognitive motivationorientations may comprise receiving a tuning corpus of tuning documents,with each tuning document comprising a plurality of meaningfullyarranged words and having a respective document-level annotationidentifying a dominant cognitive motivation orientation set for thattuning document. The method may obtain, for each tuning document,document raw confidence weight scores for each cognitive motivationorientation. The document raw confidence weight scores may be obtainedby, for each tuning document, identifying each indicator n-gramappearing in that tuning document, and, for each identified indicatorn-gram, incrementing a document raw confidence weight score for thecognitive motivation orientation associated with that identifiedindicator n-gram by the corresponding confidence weight. The method canthen normalize the document raw confidence weight scores for eachcognitive motivation orientation to obtain, for each tuning document,normalized document confidence weight scores for each cognitivemotivation orientation. The method can use the normalized documentconfidence weight scores to select, for each cognitive motivationorientation, a normalized dominance threshold minimizing a number ofincorrectly classified tuning documents. In one embodiment, incorrectclassification for a given tuning document with respect to a particularcognitive motivation orientation means that either:

-   -   (a) the normalized document confidence weight score for that        cognitive motivation orientation exceeds the normalized        dominance threshold where that cognitive motivation orientation        is absent from the document-level annotation for that tuning        document; or    -   (b) the normalized document confidence weight score for that        cognitive motivation orientation is less than or equal to the        normalized dominance threshold where that cognitive motivation        orientation is present in the document-level annotation for that        tuning document.

The present disclosure also describes computer systems and computerprogram products embodying the above methods.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the invention will become more apparent fromthe following description in which reference is made to the appendeddrawings wherein:

FIG. 1 is a flow chart showing an exemplary computer-implemented method100 for analyzing text;

FIG. 2 is a flow chart showing a first exemplary computer-implementedmethod for receiving an analysis of text;

FIG. 3 is a flow chart showing a second exemplary computer-implementedmethod for receiving an analysis of text;

FIG. 4 shows exemplary computer systems and an exemplary networkconfiguration;

FIGS. 5A, 5B and 5C show an exemplary method for neurolinguisticallyanalyzing text;

FIG. 6 is a flow chart showing an exemplary method for building ananalysis database associating n-grams with cognitive motivationorientations;

FIG. 7 is a flow chart showing an exemplary method for applying a seriesof relevance filters to the indicator candidate n-grams to obtainindicator n-grams;

FIG. 8 is a flow chart showing an exemplary method for building anormalized dominance threshold for a cognitive motivation orientation;

FIG. 9 is a flow chart showing an exemplary method for selecting anormalized dominance threshold for a cognitive motivation orientation;

FIG. 10 is a schematic representation of an exemplary computer system,which may be used in implementing various methods described herein;

FIG. 11 is a schematic representation of an exemplary smartphone, whichmay be used in implementing various methods described herein; and

FIG. 12 is a flow chart showing an exemplary method for calculating afit score.

DETAILED DESCRIPTION

Reference is now made to FIG. 1, which is a flow chart showing, at ahigh level, an exemplary computer-implemented method 100 for analyzingtext. At step 102, the method 100 receives a meaningful first textsequence, which may be any one of a variety of different types of textsequence as described further below. Within the context of thisdocument, a text sequence is “meaningful” when it conveys meaning to aliterate human reader. As such, a meaningful text sequence may be acomplete document including a plurality of paragraphs, a singleparagraph, a few sentences, a single sentence, a sentence fragment oreven an abbreviated construct. For example, a multi-paragraph letter ore-mail message is a meaningful text sequence, and the sequence “txt ul8r” is also a meaningful text sequence (meaning “I will send you a textmessage later”). So-called “emoticons”, which are sequences of ASCIIcharacters that form primitive images to convey emotional information,can also be, or form part of, meaningful text sequences. Examples ofemoticons include (quotations and separating commas do not form part ofthe emoticon) “<3”, which represents a heart, “:)”, “: )” or “:-)”,which each represent a happy face, “;)”, “; )” or “;-)”, which eachrepresent winking, and “:p”, “: p”, “:-p”, “:P”, “: P”, or “:-P”, whicheach represent the sender sticking his or her tongue out at therecipient. There are of course many other emoticons, which will befamiliar to one skilled in the art and to most teenagers with modernmobile phones. A meaningful text sequence may originate in non-textualform, for example by automated transcription from vocal communication.

At step 104, the method 100 neurolinguistically analyzes the first textsequence to generate first statistical information representingcognitive motivation orientations expressed within the first textsequence. The first statistical information may be sent to a differentcomputer system for further processing, or may be further processed bythe computer system implementing the method 100. As will be explained inmore detail below, in preferred embodiments complicated morphological,syntactic and semantic analysis is avoided in favor of statisticalmethods based on annotated training data.

As noted above, in this specification the term “cognitive motivationorientation” refers to factors, patterns and/or elements that describehow a person thinks, becomes motivated and makes decisions in a givencontext, as determined from the language used in that context. Thus, oneor more cognitive motivation orientations may be expressed within agiven meaningful text sequence. The informal term “motivation trigger”has also been used to refer to cognitive motivation orientations, andhas the same meaning. A cognitive motivation orientation is differentfrom a personality profile or psychological profile, such as oneassigned according to the DISC personality theory, Enneagram theory oranother theory, in that a cognitive motivation orientation relates to anindividual's orientation in a particular context, whereas a personalityprofile or psychological profile attempts to provide an overallcharacterization of an individual applicable across most or allcontexts. Even in the case of a personality profile or psychologicalprofile which makes use of neurolinguistic analysis, the analysis isdirected to providing an overall characterization of the individual'spsychology or personality, rather than an assessment of the motivationand orientation in a specific context. Thus, the term “cognitivemotivation orientation”, as used herein, is to be understood as beinglimited to a particular context in relation to which the person hasexpressed the language being considered; as such a cognitive motivationorientation is language-context limited.

As shown in FIG. 1, in a preferred embodiment, step 104 ofneurolinguistically analyzing the first text sequence to generate firststatistical information representing cognitive motivation orientationsexpressed within the first text sequence comprises a sub-step 104A ofextracting elements from the first text sequence, a sub-step 104B ofconducting a first comparison by comparing the elements extracted fromthe first text sequence to predetermined elements associated withparticular cognitive motivation orientations and a sub-step 104C ofderiving the first statistical information from the first comparison.The first statistical information generated at step 104 may be used todetermine a dominant cognitive motivation orientation set expressed inthe first text sequence. For a given meaningful text sequence, there maybe one or more cognitive motivation orientations that are dominant, orno cognitive motivation orientation may be dominant. Accordingly, a“dominant cognitive motivation orientation set” is a set of zero (null),one or more than one cognitive motivation orientations which aredominant for a particular meaningful text sequence. At step 105, themethod 100 determines a first dominant cognitive motivation orientationset expressed within the first text sequence based on the firststatistical information. Alternatively, where the first statisticalinformation is sent to a different computer system, that differentcomputer system may carry out step 105.

Details of exemplary implementations of steps 104 and 105 will bedescribed below in the context of FIGS. 5A, 5B and 5C.

Following step 105, the method 100 may then proceed to one or more of anumber of possible steps.

The method 100 may proceed to step 106A to communicate the dominantcognitive motivation orientation set determined at step 105, for exampleto a user of the computer system executing the method 100 or to anothercomputer system. Step 106A could be used to help a human user compose ane-mail, letter, text message or other text sequence, for example anadvertising or marketing document, that is not necessarily responsive toany other text sequence. For example, a user may be trying to compose atext sequence that has a specific dominant cognitive motivationorientation set, and step 106A can communicate whether or not that userhas succeeded.

The method 100 may proceed to step 106B to provide recommendations forpreparation of a meaningful second text sequence addressing the firsttext sequence, with the recommendations being based on the firstdominant cognitive motivation orientation set identified at step 105.These recommendations would typically suggest language to use andlanguage to avoid; the language suggestions will generally beindependent of the substantive content. Where the first text sequence isan e-mail, letter, text message or the like, step 106B could be used tohelp a human user compose an e-mail, letter, text message or the like inresponse. In other embodiments, the text may be a transcription of aportion of a conversation. For example, software implementing the method100 may be used in association with automated speech-to-text software togenerate recommendations for a human customer service agent to respondto a verbal question or statement. Thus, the response to the first textsequence may be a verbal response.

The method 100 could proceed to step 106C to automatically select apredetermined response to the first text sequence from a set ofpredetermined responses based on the dominant cognitive motivationorientation set expressed within the first text sequence. For example,step 106C could be used where the method 100 is used in association witha natural language processing system for handling customer queries. Thefirst text sequence could be a customer query, and the natural languageprocessing system could parse this customer query to identify a class ofpredetermined responses that are responsive to the query, and step 106Ccould select from within the class of predetermined responses theparticular predetermined response that best fits the dominant cognitivemotivation orientation set expressed within the customer query. Thefirst text sequence could also be a search string, for example enteredinto an Internet or other search engine, and the set of predeterminedresponses may be a set of advertisements each targeted toward aparticular cognitive motivation orientation. Alternatively, the method100 could proceed to step 106D to automatically dynamically generate aresponse to the first text sequence based at least in part on thedominant cognitive motivation orientation set expressed within the firsttext sequence. For example, in addition to choosing the information toconvey based on the information requested in a query, a virtual digitalassistant may select the language used to respond to the query based onthe dominant cognitive motivation orientation set expressed within thefirst text sequence.

In a particular embodiment, after presenting recommendations for aresponse to the first text sequence based on the dominant cognitivemotivation orientation set expressed within the first text sequence atstep 106B, the method 100 can proceed to step 108. Alternatively, step106B may be omitted and the method may proceed directly from step 104 tostep 108. At step 108 the method 100 receives a meaningful second textsequence. For example, the first text sequence may be an e-mail message,a text message or a letter, and the second text sequence may be anunsent e-mail message, text message or letter response to the first textsequence. “Unsent” in this context means that the second text sequencehas not been sent to the intended recipient, that is, the originator ofthe first text sequence, although it may have been sent to the computersystem implementing the method 100 from a different computer system. Insome embodiments, the second text sequence may be in a differentcommunication format than the first text sequence. For example, a lettermay be sent in response to an e-mail or vice versa, an e-mail messagemay be sent in response to a text message, and so on.

After receiving the second text sequence at step 108, the method 100then proceeds to step 110, where the method 100 neurolinguisticallyanalyzes the second text sequence to generate second statisticalinformation representing cognitive motivation orientations expressedwithin the second text sequence. Step 110 would typically be carried outin a manner identical to or analogous to the neurolinguistic analysis ofthe first text sequence at step 104. The method 100 then proceeds tostep 111, where the method 100 determines a second dominant cognitivemotivation orientation set expressed within the second text sequencebased on the second statistical information. Following step 111, at step112 the method 100 tests the fit between the first dominant cognitivemotivation orientation set (expressed within the first text sequence)and the second dominant cognitive motivation orientation set (expressedwithin the second text sequence). A more detailed explanation of anexemplary technique for testing fit between two cognitive motivationorientation sets is provided below in the context of FIGS. 5A, 5B and5C. Responsive to a determination that the second dominant cognitivemotivation orientation set fits the first dominant cognitive motivationorientation set, at optional step 114 the method 100 may provide anotification or confirmation of the fit, after which the method 100ends. At step 116, responsive to a determination that the seconddominant cognitive motivation orientation set misfits the first dominantcognitive motivation orientation set, the method 100 identifies themisfit, and may, at optional step 118, present recommendations formodifying the second text sequence so that the second dominant cognitivemotivation orientation set, as expressed in the second text sequence asmodified, better fits the first dominant cognitive motivationorientation set.

The first text sequence may be received (step 102) in a number of ways.For example, the first text sequence may be input directly into acomputer system executing the method 100. For example, a human user maytype the first text sequence, or dictate it using speech-to-textsoftware, that is, the first text sequence may be a transcription of averbal communication. The first text sequence may also be obtained usingoptical text recognition (OCR) software to extract the text sequencefrom an image file, such as an image of a hard copy document obtainedusing a peripheral scanner, by extracting the text sequence from anexisting file, or other suitable techniques. Thus, software implementingthe method 100 may be provided for a desktop computer, laptop computer,tablet computer, smartphone or other computer system.

Alternatively, the first text sequence, and the second text sequence, ifany, may be received at the computer system executing the method 100from a different computer system, for example in a client-serverarchitecture. FIG. 2 is a flow chart showing at a high level a firstexemplary computer-implemented method 200 for receiving an analysis oftext. The method 200 may be implemented by a first computer system thatis in communication with a second computer system that is executing themethod 100. At step 202, the first computer system transmits ameaningful first text sequence to the second computer system. The secondcomputer system may then, for example, execute an implementation of themethod 100. At step 204, the first computer system receives from thesecond computer system a communication responsive to at least a resultof automated neurolinguistic analysis of cognitive motivationorientations expressed within the first text sequence.

The communication received at step 204 may comprise first statisticalinformation representing cognitive motivation orientations expressedwithin the first text sequence which can be used to determine the firstdominant cognitive motivation orientation set, or may comprise anidentification of the neurolinguistically-determined first dominantcognitive motivation orientation set.

The communication received at step 204 may be responsive to additionalfactors beyond the neurolinguistically-determined dominant cognitivemotivation orientation set expressed within the first text sequence. Forexample, as described above, in a virtual digital assistant context thecommunication may also be responsive to the specific informationrequested in a query. Similarly, where the first text sequence is asearch term entered into a search engine such as Google, thecommunication received at step 204 may also be responsive to the searchterm in addition to the neurolinguistically-determined dominantcognitive motivation orientation set expressed within the first textsequence. As another example, the communication received at step 204 maycomprise a set of search results whose contents and/or organization areinfluenced by both the search term and theneurolinguistically-determined dominant cognitive motivation orientationset expressed within the first text sequence.

The communication received at step 204 may comprise a predeterminedresponse to the first text sequence that is automatically selected froma set of predetermined responses based on the result of automatedneurolinguistic analysis of the first text sequence. For example, thecommunication received at step 204 may comprise advertisements which arebased on the search term and whose language is based on, or selectedbased on, the neurolinguistically-determined dominant cognitivemotivation orientation set expressed within the first text sequence.Similarly, the first text sequence may be an e-mail message sent througha Web-based e-mail service like Google's “Gmail” service or Microsoft'sHotmail service or another such service and the communication receivedat step 204 may be a targeted advertisement presented as part of thereturned Web page. In such an embodiment, the category of advertisement(e.g. the company or type of product) may be selected based on specificterms used in the e-mail message, and the language used may be based on,or selected based on, the neurolinguistically-determined dominantcognitive motivation orientation expressed in the e-mail message.

Consider a case where a user has entered the search term “rain boots”into an Internet search engine, or the text scanning tool of a Web-basede-mail service has detected the phrase “I need to buy new rain boots” inan e-mail message. This is sufficient to determine that an advertisementfor rain boots should be presented to the user. A server system maystore a plurality of different advertisements for rain boots from aparticular manufacturer, and automatically select which advertisement topresent based on the neurolinguistically-determined dominant cognitivemotivation orientation set. For example, the server may storeadvertisements which use specific language targeted to specific dominantcognitive motivation orientation sets, as shown in the table below(rather than all possible cognitive motivation orientation sets, onlycertain exemplary dominant cognitive motivation orientation sets areshown for ease of illustration):

Dominant Cognitive Motivation Orientation Set Language of AdvertisementAway From Tired of getting your feet wet? Towards Want dry feet?External You need dry feet. Internal Would you like dry feet? ProceduresHere's how to get dry feet. Options Many ways to get dry feet. Options &Away From Many ways to avoid wet feet. Procedures & Away From Here's howto avoid wet feet. External & Away From Avoid wet feet.

More details about the exemplary cognitive motivation orientations inthe table above are provided below.

In addition to cases where a targeted advertisement is presented inresponse to a search term, the content of an e-mail message or anothermeaningful text sequence, automatically selecting from a set ofpredetermined responses based on the result of automated neurolinguisticanalysis of the first text sequence could be used where the firstcomputer system is that of a customer or potential customer and thesecond computer system is that of a product or service provider. Inthese types of embodiments, the communication received at step 204 maybe a “canned” advertisement, offer or other commercial material selectedby the second computer system because its language fits theneurolinguistically-determined dominant cognitive motivation orientationset expressed within the first text sequence.

Alternatively, the communication received at step 204 may be anautomatically dynamically generated response to the first text sequencebased on the result of automated neurolinguistic analysis of the firsttext sequence. In such an embodiment, the language used to build thedynamically generated response may be based on in which the language isselected based on the neurolinguistically-determined dominant cognitivemotivation orientation set.

Similarly to the method 100, following step 204, the method 200 may thenproceed to one or more of a number of possible steps.

At optional step 206A, the method 200 may communicate the dominantcognitive motivation orientation set to a user of the computer systemexecuting the method 200; in such an embodiment the communicationreceived at step 204 would identify the dominant cognitive motivationorientation set.

The method 200 may proceed to optional step 206B to providerecommendations for preparation of a meaningful second text sequence,such as an e-mail, text message, letter or the like, or for suggestionsfor a verbal communication, addressing the first text sequence. In suchembodiments, the communication received at step 204 could identifyrecommendations for a response to the first text sequence either byinclusion of those recommendations within the communication, by use of atag or code, such as “recommendation 5”, that designates one or morepredefined or “canned” recommendations stored on the first computersystem, or the first computer system may determine whichrecommendation(s) to present based on the neurolinguistically-determinedfirst dominant cognitive motivation orientation set. In each case, therecommendations identified by the communication received at step 204 arebased on the neurolinguistically-determined first dominant cognitivemotivation orientation set. Step 206A may be omitted and the method mayproceed directly to step 206B to provide recommendations withoutexplicitly identifying the first dominant cognitive motivationorientation set, or steps 206A and 206B may proceed substantially inparallel.

The method 200 may also proceed to step 206C to present information to auser of the first computer system. The nature of the informationcommunicated will depend on the nature of the communication received atstep 204. For example, the communication may be search results, anadvertisement, or other information.

After step 206C or step 206A and/or step 206B, if present, or after step204, the method 200 ends.

Reference is now made to FIG. 3, which is a flow chart showing at a highlevel a second exemplary computer-implemented method 300 for receivingan analysis of text. The method 300 may be implemented by a firstcomputer system that is in communication with a second computer systemthat is executing an embodiment of the method 100 that includes steps106B to 118. The method 300 may be carried out independently, or may bea continuation of the method 200 and begin after step 204, 206A or 206B.At step 302, the first computer system transmits a meaningful first textsequence to the second computer system, and at step 303 the firstcomputer system transmits a meaningful second text sequence to thesecond computer system. The first and second text sequences may beobtained, for example, using any of the techniques described above. Asin the case of the method 100, the first text sequence may be an e-mailmessage, a text message or a letter, and the second text sequence may bean unsent e-mail message, text message or letter response to the firsttext sequence. Steps 302 and 303 may be performed in reverse sequence,or substantially simultaneously. In a case where the method 300 is acontinuation of the method 200, the first text sequence transmitted atstep 302 will typically be the same text sequence transmitted at step202. As such, in stateful embodiments, step 302 may be omitted when thefirst text sequence was already transmitted at step 202. After receivingthe first and second text sequences sent by the first computer system atsteps 302 (or 202) and 303, the second computer system may then, forexample, execute an implementation of the method 100 that includes steps106B to 118. At step 304, the first computer system receives from thesecond computer system a communication that is responsive both to theresult of automated neurolinguistic analysis of the cognitive motivationorientations expressed in the first text sequence and to a result ofautomated neurolinguistic analysis of cognitive motivation orientationsexpressed in the second text sequence. The communication received atstep 304 may comprise statistical information representing cognitivemotivation orientations expressed within the first and second textsequences which can be used by the first computer system to determinethe dominant cognitive motivation orientation sets for the first andsecond text sequences, or may comprise an identification of theneurolinguistically-determined dominant cognitive motivation orientationsets expressed in the first and second text sequences. In either case,the first computer system could then execute a test of fit between theneurolinguistically-determined first dominant cognitive motivationorientation set expressed within the first text sequence and aneurolinguistically-determined second dominant cognitive motivationorientation set expressed within the second text sequence at optionalstep 305. Alternatively, the communication received at step 304 may beresponsive to the outcome of a test of fit, carried out by the secondcomputer system, between the neurolinguistically-determined firstdominant cognitive motivation orientation set expressed within the firsttext sequence and a neurolinguistically-determined second dominantcognitive motivation orientation set expressed within the second textsequence. In one embodiment, the communication received at step 304identifies at least one recommendation for modifying the second textsequence so that the neurolinguistically-determined second dominantcognitive motivation orientation set expressed in the second textsequence, as modified, fits the neurolinguistically-determined firstdominant cognitive motivation orientation set. As noted above, therecommendations may be identified by inclusion in the communicationreceived at step 304, by use of a tag or code that designates one ormore predefined or “canned” recommendations stored on the first computersystem, or the first computer system may determine the recommendationsdirectly based the dominant cognitive motivation orientation sets.

After step 304, or after step 305 if present, the method 300 proceeds tostep 306 to present information to a user of the first computer system.The nature of the information communicated will depend on the nature ofthe communication received at step 304 and whether step 305 is present.For example, at step 306 the method 300 may communicate the results of atest of fit between the first and second dominant cognitive motivationorientation sets, either with or without recommendations for modifyingthe second text sequence.

Reference is now made to FIG. 4, which shows exemplary computer systemsand an exemplary network configuration which may be used in implementingthe above-described methods. In FIG. 4, a server 402, which may comprisea plurality of individual computers, implements an embodiment of themethod 100. A plurality of devices, including one or more smartphones404, desktop computers 406, laptop computers 408 and tablet computers410, implement either the method 200 or the method 300, and communicatevia a network 412, in this case the Internet, with the server 402. Eachof the devices 404, 406, 408, 410 is a first computer system and theserver 402 is a second computer system, and the devices 404, 406, 408,410 can each transmit a communication 414 containing a meaningful firsttext sequence, as well as a second meaningful text sequence where themethod 300 is being implemented, to the server 402 for analysis. Theserver 402 can transmit communications 416 to the respective devices404, 406, 408, 410 based on the analysis.

For example, in one embodiment, a network architecture as shown in FIG.4 can be used in association with an implementation of the method 100that includes steps 106B through 118 on the server 402 and animplementation of the method 200 and/or 300 on any of the devices 404,406, 408, 410 to analyze e-mail messages. In this embodiment, themethods 100 and 300, executed together, would identify the dominantcognitive motivation orientation set expressed in incoming emails,explain them to the user, provide recommendations for vocabulary to useand to avoid, and then analyze the corresponding response in order tomake sure that the dominant cognitive motivation orientation setexpressed in the response fits the dominant cognitive motivationorientation set expressed in the incoming e-mail message, therebyimproving the communication.

A plug-in may be installed on an e-mail client executing on any of thedevices 404, 406, 408, 410 and configured to communicate via a secureconnection with the server 402. A plug-in is a piece of software whichenhances another software application and usually cannot be runindependently; in this case the plug-in integrates with the e-mailclient and executes an implementation of the method 300, and the server402 executes software that implements an embodiment of the method 100.The plug-in may, for example, provide one or more additional virtualbuttons on the user interface for the e-mail client which, whenactivated, trigger the method 200 or 300. Preferably, the server 402presents an application programming interface (API) that is supportedacross platforms so that different e-mail clients on different devicescan be supported. In one preferred embodiment, communication between thedevices 404, 406, 408, 410 and the server 402 is stateless, i.e. theserver does not store information from, nor does it correlateinformation to, particular users. Thus, where the method 300 is acontinuation of the method 200, the first text sequence would be re-sentat step 302 even though it was already sent at step 202 since it wouldnot have been stored by the server. In other embodiments thecommunication may be stateful and step 302 could be omitted.Communication between the devices 404, 406, 408, 410 and the server 402is preferably synchronous, meaning that the response is received as theresult of the client query. In a preferred embodiment, the plug-infacilitates communication between the devices 404, 406, 408, 410 and theserver 402 over the network 412 using the Representational StateTransfer (REST) framework which supports the HTTP transport protocol andencryption, as it preferable for both client requests and serverresponses to be encrypted.

In one embodiment, the server 402 may be a different server from theserver that administers the e-mail system with which the e-mail clientis associated. For example, the server 402 may be the server of a thirdparty service provider which provides the neurolinguistic analysis usinga multi-tenant Software as a Service (SaaS) architecture. Alternatively,the server 402 may be the same server that administers the e-mail systemwith which the e-mail client is associated.

The use of a plug-in for the e-mail client implements a thin clientapproach, in which the plug-in would carry out very little processing,for example extracting the text sequence to be analyzed from the e-mail.E-mail messages often include information which is extraneous to themessage text itself, such as headers, formatting such as bolding anditalics, and non-text elements such as images and attachments. Moreover,an e-mail message may also include quoted text from previous e-mailmessages which generally should not be included in the text to beanalyzed. Thus, in one embodiment the plug-in for the e-mail clientwould extract the text sequence to be analyzed from the e-mail in amanner which excludes extraneous information and quoted text, leavingonly plain text. In an embodiment that implements the method 300, theplug-in may extract both the text of the unsent response as well as thefirst level of quoted text, which would be the e-mail to which theunsent response is responsive. Where an e-mail message is forwardinganother e-mail message, the e-mail client may provide a choice as towhether to analyze the forwarded e-mail message or the added text. Thus,the e-mail client may send a text sequence representing the content ofan e-mail that has been received, two text sequences representing thecontent of an unsent response and the content of the e-mail to which theunsent response is responsive, or a text sequence representing an unsente-mail that is not responsive to any other e-mail.

In one embodiment in which the method 100 and the methods 200 and/or 300are used to analyze e-mail, after carrying out step 104 and, ifapplicable, step 110, the server 402 sends a response to the e-mailclient that includes one or more numerical values for one or morecorresponding cognitive motivation orientations. Numerical values may beprovided for all cognitive motivation orientations tested, or only forthose determined to be dominant. The numerical values will indicate therelative rankings of the cognitive motivation orientations, and can beused by the e-mail client to identify the most dominant cognitivemotivation orientations. In this embodiment, the response sent by theserver 402 to the e-mail client may also include explanation textregarding the dominant cognitive motivation orientation set(s) and, inthe case of the method 300, recommendation text for adjusting a replye-mail. The explanation text and recommendation text may be provided ina single format, or in both a short format and a longer, more detailedformat. In other embodiments, the explanation text and recommendationtext may be stored locally by the e-mail client. In either case, thee-mail client can use the numerical values to determine the dominantcognitive motivation orientation set(s) and select the appropriateexplanation text and recommendation text where applicable. In anembodiment in which the explanation and recommendation text is storedlocally, the server 402 may send an identifier, such as an alphanumericidentifier, of the dominant cognitive motivation orientation set(s) andthe e-mail client can select which unit of explanation text orrecommendation text to present based on that identifier. In still otherembodiments, the server 402 may send only the specific explanation text(and recommendation text in the case of the method 300) applicable todominant cognitive motivation orientation set(s) determined by theserver 402. In yet further embodiments, the server 402 may send thestatistical information required for the device 404, 406, 408, 410 todetermine the dominant cognitive motivation orientation set(s). Theserver 402 can also send data representing a confidence value, such as apercentage, indicating the degree of confidence in the determinationthat each cognitive motivation orientation in the dominant cognitivemotivation orientation set is in fact dominant.

Thus, in an embodiment which analyzes e-mail, a user of the e-mailclient can request analysis of an e-mail, which may be a receivede-mail, an unsent reply to a received e-mail, or an unsent e-mail thatdoes not reply to another e-mail. The e-mail client can then provide tothe user explanations of the dominant cognitive motivationorientation(s) in the e-mail(s), for example as prefabricated text in apop-up window. For a received e-mail, the e-mail client can providerecommendations for preparing a response expressing a dominant cognitivemotivation orientation set that will match the dominant cognitivemotivation orientation set expressed in the received e-mail. For anunsent reply to a received e-mail, the e-mail client can providerecommendations for modifying the unsent reply based on a comparison ofthe dominant cognitive motivation orientation set expressed in thereceived e-mail to the dominant cognitive motivation orientation setexpressed in the unsent reply; these recommendations may also beprovided as prefabricated text in a pop-up window. A user can repeatedlyiterate the method 300 as they modify the unsent response to the e-mailto see whether the response is improving. In a preferred embodiment,when a user activates the “send” function for an unsent e-mail reply,the e-mail client can check whether the dominant cognitive motivationorientation set for the unsent reply has been checked for fit with thedominant cognitive motivation orientation set of the original e-mail,and provide a confirmation window if it has not (e.g. “are you sure youwant to send this reply without checking for fit?”). Confidence valuesfor the cognitive motivation orientations in the dominant cognitivemotivation orientation set may also be displayed by the e-mail client.For example, an average of the confidence values for each of thecognitive motivation orientations in the dominant cognitive motivationorientation set may be presented graphically as a bar, which may includered, yellow and green portions corresponding to increasing levels ofconfidence.

In a client-server embodiment such as that shown in FIG. 4, the server402 may also gather and correlate information about relating tocognitive motivation orientations with various other data items. Forexample, the server 402 may determine the frequency with which variouscognitive motivation orientations or cognitive motivation orientationsets occur within a given geographic location, or within specificdemographic groups.

In addition to the thin client approach described above in the contextof FIG. 4, the method 100 may be implemented on the same computer systemas the methods 200 and/or 300.

FIGS. 5A, 5B and 5C show an exemplary method 500 for neurolinguisticallyanalyzing text. The method 500 is a particular exemplary implementationof an embodiment of the method 100 which includes steps 106B to 118 inaddition to steps 102 and 104. The method 500 may be carried out on asingle computer system, or certain steps of the method 500 may becarried out on different computer systems in communication with oneanother.

At step 502, the method 500 receives a meaningful first text sequence.This step corresponds to step 102 of the method 100. For example, in ane-mail embodiment the first text sequence received at step 502 may bethe extracted message content of a received e-mail or an unsent e-mailin plain text form.

At step 504, the method 500 derives first text sequence n-grams from thefirst text sequence. In the exemplary embodiment, the first textsequence n-grams consist of all possible n-grams in the first textsequence. An “n-gram” is a contiguous sub-sequence of n items from agiven sequence, and in the context of a text sequence may be phonemes,syllables, letters or words. In a presently preferred embodiment, theitems are words and n-grams of one word (a “unigram”), two words (a“bigram”) and three words (a “trigram”). In other embodiments, longer orshorter n-grams may be used. In a current embodiment, n-gram derivationis based on an interval [a, b] and thus all n-grams of size a, a+1, a+2,. . . , b are derived. In a presently preferred embodiment the intervalis [1, 3] although this interval is configurable. Derivation of wordn-grams may be facilitated by tokenizing the first text sequence. Theterm “tokenizing” refers to a process in which a sequence of characters,such as ASCII characters, is divided into a sequence of individualtokens, such as words. Techniques for tokenizing, and for extractingn-grams from a sequence of tokens, are well known in the field ofcomputer science, and are not discussed further here. By tokenizing thefirst text sequence into a sequence of words and then iterating overthat sequence of words, all of the word unigrams, word bigrams and wordtrigrams can be identified. For example, if the first text sequence is“I don't want to go there”, step 504 would derive the following firsttext sequence n-grams: “I”, “I don't”, “I don't want”, “don't”, “don'twant”, “don't want to”, “want”, “want to”, “want to go”, “to”, “to go”,“to go there”, “go”, “go there”, and “there”. The method 500 can handleemoticons by treating the emoticons as words; the tokenization processwould be adapted so that the characters building an emoticon are groupedinto a single token.

At step 506, the method 500 advances to the next (or first) first textsequence n-gram and then moves to step 508. At step 508, the method 500compares the current first text sequence n-gram to a plurality ofpredetermined indicator n-grams to see if the current first textsequence n-gram matches one of the predetermined indicator n-grams. Eachof the predetermined indicator n-grams is associated with at least onecognitive motivation orientation, and each predetermined indicatorn-gram has, for each cognitive motivation orientation with which it isassociated, a corresponding cognitive motivation orientation confidenceweight. In a presently preferred embodiment each predetermined indicatorn-grams is associated with exactly one cognitive motivation orientation.In alternate embodiments some predetermined indicator n-grams may beassociated with more than one cognitive motivation orientation, with adifferent cognitive motivation orientation confidence weight for eachcognitive motivation orientation with which it is associated. A flexibledata structure could provide a cognitive motivation orientationconfidence weight variable for each possible cognitive motivationorientation, with the variable set to zero for cognitive motivationorientations with which the respective predetermined indicator n-gram isnot associated. In a presently preferred embodiment, the predeterminedindicator n-grams are stored in a specially formatted text file; otherfile types may also be used to store the predetermined indicatorn-grams, for example a relational database.

At step 510, responsive to a determination at step 508 that the currentfirst text sequence n-gram does not match any of the predeterminedindicator n-grams, the method 500 proceeds to step 510 to check whetherthere are any additional first text sequence n-grams that have not yetbeen compared. Responsive to a “yes” determination at step 510, themethod 500 returns to step 506 to advance to the next n-gram.

Responsive to a determination at step 508 that the current first textsequence n-gram matches one of the predetermined indicator n-grams, atstep 512 the method 500 records the corresponding cognitive motivationorientation confidence weight for each cognitive motivation orientationassociated with that predetermined indicator n-gram. In the illustratedembodiment, recording the cognitive motivation orientation confidenceweight is accomplished by, at step 512, incrementing a correspondingfirst text sequence cognitive motivation orientation weight score foreach cognitive motivation orientation associated with that predeterminedindicator n-gram according to the corresponding cognitive motivationorientation confidence weight. In a presently preferred embodiment inwhich each predetermined indicator n-gram is associated with exactly onecognitive motivation orientation, step 512 will increment the first textsequence cognitive motivation orientation weight score for the cognitivemotivation orientation associated with the predetermined indicatorn-gram that matches the current first text sequence n-gram. After step512, the method 500 proceeds to step 510 to check whether there are anyadditional first text sequence n-grams that have not yet been compared.

If the method 500 determines at step 510 that there are no more firsttext sequence n-grams that have not yet been compared, that is, that allof the first text sequence n-grams have been checked against theplurality of predetermined indicator n-grams, this means that the method500 has determined the “raw” first text sequence cognitive motivationorientation weight scores for each cognitive motivation orientation.Further processing of the “raw” first text sequence cognitive motivationorientation weight scores may be carried out on the same computer systemthat calculated them, or the “raw” first text sequence cognitivemotivation orientation weight scores may be sent to another computersystem for further processing.

Other techniques may be used for recording the corresponding cognitivemotivation orientation confidence weight for each cognitive motivationorientation associated with a predetermined indicator n-gram matchingthe current n-gram. For example, in an embodiment in which processing isdivided across two or more computer systems, one computer system mayannotate the first text sequence, with the n-grams that matched apredetermined indicator n-gram being annotated with the correspondingcognitive motivation orientation confidence weights. Once all n-gramshave been examined, the annotated first text sequence could then be sentto another computer system, which could extract the annotated cognitivemotivation orientation confidence weights to obtain the “raw” first textsequence cognitive motivation orientation weight scores for furtherprocessing, for example in accordance with subsequent steps of themethod 500.

The “raw” first text sequence cognitive motivation orientation weightscores will depend on the size of the first text sequence, and thereforefollowing a determination at step 510 that all of the first textsequence n-grams have been checked against the predetermined indicatorn-grams, the method 500 then proceeds to step 514, where the method 500normalizes the first text sequence cognitive motivation orientationweight scores to obtain normalized first text sequence cognitivemotivation orientation weight scores. The first text sequence cognitivemotivation orientation weight scores may be normalized, for example, bydividing each first text sequence cognitive motivation orientationweight score by the number of tokens in the first text sequence. Othernormalization procedures may also be used. The method 500 then proceedsto step 516.

Each of the cognitive motivation orientations which the method 500detects will have a corresponding normalized dominance threshold whichserves as a boundary for determining whether or not that cognitivemotivation orientation is dominant for a particular text sequence. Anexemplary method for building normalized dominance thresholds will bedescribed below in the context of FIG. 8.

At step 516, the method 500 advances to the next (or first) normalizedfirst text sequence cognitive motivation orientation weight score andthen to step 518 to advance to the corresponding normalized dominancethreshold. The method 500 then proceeds to step 520 and compares thecurrent normalized first text sequence cognitive motivation orientationweight score to the corresponding normalized dominance threshold to seewhether the current normalized first text sequence cognitive motivationorientation weight score exceeds the corresponding normalized dominancethreshold. Equivalently, depending on the value of the normalizeddominance threshold, step 520 may test whether the current normalizedfirst text sequence cognitive motivation orientation weight score isequal to or greater than the corresponding normalized dominancethreshold. Preferably, where the current normalized first text sequencecognitive motivation orientation weight score exceeds the correspondingnormalized dominance threshold, step 520 will also determine and storethe amount by which the current normalized first text sequence cognitivemotivation orientation weight score exceeds the corresponding normalizeddominance threshold.

Responsive to a determination at step 520 that the current normalizedfirst text sequence cognitive motivation orientation weight scoreexceeds the corresponding normalized dominance threshold, the method 500proceeds to step 522. At step 522, the method 500 identifies thecognitive motivation orientation for the current normalized first textsequence cognitive motivation orientation weight score as being adominant cognitive motivation orientation, for example by setting aflag. Where step 520 also determines the amount by which the currentnormalized first text sequence cognitive motivation orientation weightscore exceeds the corresponding normalized dominance threshold, storinga non-zero value for this amount may serve to identify the relevantcognitive motivation orientation as dominant.

If method determines at step 520 that the current normalized first textsequence cognitive motivation orientation weight score does not exceedthe corresponding normalized dominance threshold, the method 500proceeds to step 524. At step 524, the method 500 identifies thecognitive motivation orientation for the current normalized first textsequence cognitive motivation orientation weight score as being anon-dominant cognitive motivation orientation. In an embodiment in whichstep 520 also determines the amount by which the current normalizedfirst text sequence cognitive motivation orientation weight scoreexceeds the corresponding normalized dominance threshold; in someembodiments storing a zero value or a negative value for this amount mayserve to identify the relevant cognitive motivation orientation asnon-dominant.

After either of steps 522 or 524, the method 500 proceeds to step 526 tocheck whether there are any normalized first text sequence cognitivemotivation orientation weight scores that have not yet been comparedagainst their corresponding normalized dominance threshold. Responsiveto a “yes” determination at step 526, the method 500 returns to step 516to advance to the next (or first) normalized first text sequencecognitive motivation orientation weight score. Responsive to a “no”determination at step 526, meaning that all the normalized first textsequence cognitive motivation orientation weight scores have beencompared against their corresponding normalized dominance threshold, themethod 500 proceeds to step 528.

At step 528, the method 500 ranks the dominant cognitive motivationorientations for the first text sequence according to the differencebetween each normalized first text sequence cognitive motivationorientation weight scores and the respective corresponding normalizeddominance threshold. To account for the different magnitudes of thenormalized dominance threshold, the ranking process includes a scalingstep. In one embodiment, each normalized first text sequence cognitivemotivation orientation weight score may be divided by its respectivecorresponding normalized dominance threshold, with values below 1indicating non-dominance and values above 1 indicating dominance, andthe values can then be ranked. In another embodiment, this scaling maybe achieved by dividing the difference (positive or negative) betweenthe normalized first text sequence cognitive motivation orientationweight score and the corresponding normalized dominance threshold by theabsolute value of the normalized dominance threshold. In a preferredembodiment, each of the normalized first text sequence cognitivemotivation orientation weight scores is mapped to a range in which therespective normalized dominance threshold is set to zero. For example,the respective normalized first text sequence cognitive motivationorientation weight scores may be mapped according to a scaling in whichthe portion from zero to the corresponding normalized dominancethreshold is mapped to normalized range of −100 to zero. The result isthat normalized first text sequence cognitive motivation orientationweight scores above zero indicate dominance and normalized first textsequence cognitive motivation orientation weight scores equal to orbelow zero indicate non-dominance. Following this scaling step, thescaled normalized first text sequence cognitive motivation orientationweight scores can be compared and ranked. The respective scalednormalized first text sequence cognitive motivation orientation weightscores can also be used as a confidence value indicating the degree ofconfidence in the determination that the respective cognitive motivationorientation is dominant, with the distance from zero representing thedegree of confidence.

Although FIG. 5A shows step 520 as occurring before step 528, in otherembodiments the step of determining whether the current normalized firsttext sequence cognitive motivation orientation weight score exceeds thecorresponding normalized dominance threshold may occur after orsubstantially simultaneously with the step of ranking the dominantcognitive motivation orientations for the first text sequence. Incertain embodiments, steps 520 through 524 may be omitted because theranking at 528 will inherently distinguish non-dominant cognitivemotivation orientations from dominant cognitive motivation orientations.For example, where the normalized cognitive motivation orientationweight scores are divided by their respective normalized dominancethresholds, a value of 1 can be used to distinguish between dominant andnon-dominant cognitive motivation orientations, with values below orequal to 1 indicating non-dominance and values above 1 indicatingdominance. Similarly, where each of the normalized first text sequencecognitive motivation orientation weight scores is mapped to a range inwhich the respective normalized dominance threshold is set to zero, anegative or zero value will indicate non-dominance and a positive valuewill indicate dominance.

Moreover, an identification of the ranking of the cognitive motivationorientations may be sent to another computer system, without determiningwhich cognitive motivation orientations, if any, are dominant. Forexample, the values used for the ranking may be sent in an ordered listor other suitable format to such other computer system; the ranking ofthe cognitive motivation orientations will be inherent in the valuesused to rank them. The other computer system could use those values, inconjunction with appropriate boundary values, to determine whichcognitive motivation orientations are dominant. The ranking values canalso be used by the other computer system to select the m most dominantcognitive motivation orientations as described below. In oneparticularly preferred embodiment, each of the normalized first textsequence cognitive motivation orientation weight scores is mapped to arange in which the respective normalized dominance threshold is set tozero, and the resulting values are sent to another computer system as anordered list. For example, a first computer system may execute themethod 200, and a second computer system may execute steps 502 to 528(possibly omitting steps 520 to 526) and then return the valuesresulting from step 528 to the first computer system as an ordered list;the ordered list would then be the communication received at step 204 ofthe method 200. The order in which the values appear in the ordered listappear will follow a predetermined pattern so that the values can beassociated with the corresponding cognitive motivation orientations. Forexample, the first value in the list may correspond to the “toward”cognitive motivation orientation, the second value may correspond to the“away from” cognitive motivation orientation, the third value maycorrespond to the “internal” cognitive motivation orientation, and soon. Steps 530 and 532 could then be carried out by the first computersystem. More details about the foregoing cognitive motivationorientations are provided below.

After step 528, the method 500 then proceeds to step 530, where themethod 500 selects the m highest-ranked dominant cognitive motivationorientations as the dominant cognitive motivation orientation set forthe first text sequence, where m is a positive integer. In a presentlypreferred embodiment, m=2 although other values of m may be used. Alsoin the presently preferred embodiment, the dominant cognitive motivationorientation set contains the most dominant cognitive motivationorientations but the ranking of dominant cognitive motivationorientations within the dominant cognitive motivation orientation set isimmaterial. In such an embodiment, where the number of cognitivemotivation orientations is identified as dominant by steps 520 through526 is less than or equal to m, steps 528 and 530 are reduced totriviality and may be omitted; optionally an intermediate step testingfor this condition may be interposed after step 526 to permit steps 528and 530 to be bypassed where appropriate. In other embodiments, theranking of dominant cognitive motivation orientations within thedominant cognitive motivation orientation set may be relevant forsubsequent processing and as such steps 528 and 530 would only bebypassed, if at all, where only a single cognitive motivationorientation, or no cognitive motivation orientation, was identified asdominant.

The result of the method 500, following step 530 (or at the completionof all iterations of steps 520 to 526 if steps 528 and 530 arebypassed), is a dominant cognitive motivation orientation set for thefirst text sequence, which contains either the m most dominant of thecognitive motivation orientations that were identified as dominant or,if fewer than m cognitive motivation orientations were identified asdominant, all of the cognitive motivation orientations that wereidentified as dominant. If no cognitive motivation orientations wereidentified as dominant, the dominant cognitive motivation orientationset will be null or empty. As noted above, a presently preferred valuefor m is 2. Accordingly, steps 514 through 530 (or 526 if steps 528 and530 are bypassed) are an exemplary procedure for using the recordedcognitive motivation orientation confidence weights for the first textsequence, which are obtained by steps 502 through 512, to determine afirst dominant cognitive motivation orientation set expressed in thefirst text sequence. Following step 530 (or at the completion of alliterations of steps 520 to 526 if steps 528 and 530 are bypassed), themethod 500 has transformed the meaningful text sequence received at step502 into actionable information about the dominant cognitive motivationorientation(s) of the author of that text sequence.

In some embodiments, steps 502 to step 530 (or steps 502 through thefinal iteration of step 526 if steps 528 and 530 are bypassed) may beused to identify a dominant cognitive motivation orientation set for thefirst text communication for purposes other than comparing it to thedominant cognitive motivation orientation set for a second textsequence. For example, steps 102 and 104 of the method 100 may beimplemented using this portion of the method 500, and the method 100could then proceed to step 106A to communicate the dominant cognitivemotivation orientation set determined at step 104, to step 106C toautomatically select a predetermined response to the first text sequencefrom a set of predetermined responses based on the dominant cognitivemotivation orientation set expressed within the first text sequence, orto step 106D to automatically dynamically generate a response to thefirst text sequence based on the dominant cognitive motivationorientation set expressed within the first text sequence.

Following step 530, the method 500 proceeds to optional step 532, wherethe method 500 provides recommendations for preparation of a meaningfulsecond text sequence addressing the first text sequence. Therecommendations provided at step 532 are based on the dominant cognitivemotivation orientation set determined at step 530 (or at the completionof all iterations of steps 520 to 526 if steps 528 and 530 arebypassed). Step 532 may be executed in all cases or only in response toinput from a user, or another computer system, requesting therecommendations. The recommendations may be predefined or “canned”recommendations, and the appropriate recommendations may be retrievedfrom a lookup table based on the dominant cognitive motivationorientation set. Alternatively, the recommendations may be dynamicallygenerated based on the dominant cognitive motivation orientation set.The recommendations provided at step 532 may be presented on a displayof the computer system implementing the method 500, or may betransmitted to another computer system. Alternatively, step 532 maytransmit to another computer system an identification of the dominantcognitive motivation orientation set for the first text sequence, or ofthe recommendations to be presented, and that other computer canretrieve the appropriate recommendations, either from local ordistributed storage. In some embodiments, at step 532 identification ofboth the dominant cognitive motivation orientation set for the firsttext sequence and of recommendations may be transmitted.

Following step 532, or step 530 if step 532 is omitted, the method 500proceeds to step 534. At step 534 the method 500 receives a meaningfulsecond text sequence that addresses the first text sequence. Forexample, where the first text sequence is an e-mail message, the secondtext sequence may be an e-mail response to the first text sequence thathas been entered into the e-mail client but not yet sent to the personwho sent the first e-mail.

Following step 534, the method then proceeds to steps 536 through 562.At steps 536 through 562, the method 500 derives second text sequencen-grams from the second text sequence and compares the second textsequence n-grams to the plurality of predetermined indicator n-grams.For each second text sequence n-gram matching a predetermined indicatorn-gram, the method 500 records the corresponding cognitive motivationorientation confidence weight for each cognitive motivation orientationassociated with that predetermined indicator n-gram, and the method 500uses the recorded cognitive motivation orientation confidence weightsfor the second text sequence to determine a second dominant cognitivemotivation orientation set expressed in the second text sequence. Steps536 through 562 correspond to steps 506 through 530, respectively,except that steps 536 through 562 are performed on the second textsequence rather than the first text sequence. Accordingly, a detaileddiscussion of steps 536 through 562 is omitted in the interest ofbrevity. It should be noted that step 562 preferably uses the same valueof m as does step 530.

In the exemplary method 500, determination of the first and seconddominant cognitive motivation orientations is based on statisticalinformation derived from the appearance of particular indicator n-gramsin the first and second text sequences and the weight that thoseindicator n-grams carry as markers of particular cognitive motivationorientations, as determined from annotated training data (as describedfurther below). As such, complicated morphological, syntactic andsemantic analysis is avoided. Moreover, the analysis is substantiallyindifferent to the substantive meaning of the text sequence.

At the conclusion of step 562 (or at the completion of all iterations ofsteps 552 to 558 if steps 562 and 562 are bypassed), the method 500 willhave obtained a dominant cognitive motivation orientation set for thefirst text sequence and a dominant cognitive motivation orientation setfor the second text sequence. Thus, following step 562 (or at thecompletion of all iterations of steps 552 to 558 if steps 560 and 562are bypassed), the method 500 has transformed the meaningful textsequence received at step 534 into actionable information about thedominant cognitive motivation orientation(s) of the author of that textsequence, again as expressed in the dominant cognitive motivationorientation set.

The method 500 then proceeds to step 564.

At step 564, the method 500 tests whether the second dominant cognitivemotivation orientation set fits the first dominant cognitive motivationorientation set. The precise nature of the test will depend on thecategories of cognitive motivation orientations; exemplary rules forsuch a test are described below.

At step 566, responsive to a determination at step 564 that the seconddominant cognitive motivation orientation set fits the first dominantcognitive motivation orientation set, the method 500 confirms the fit atstep 566 and then ends. Confirming the fit may be done, for example, bypresenting the confirmation on a display of the computer systemimplementing the method 500, or by transmitting the confirmation toanother computer system.

At step 568, responsive to a determination at step 566 that the seconddominant cognitive motivation orientation set misfits the first dominantcognitive motivation orientation set, the method 500 identifies themisfit and then proceeds to step 570. The identification of the misfitmay optionally be presented on a display of the computer systemimplementing the method 500 or transmitted to another computer system.At step 570, responsive to identifying the misfit, the method 500provides recommendations for modifying the second text sequence to fitthe second dominant cognitive motivation orientation set for themodified second text sequence to the dominant cognitive motivationorientation set for the first text sequence, after which the method 500ends. At optional step 572, the method 500 calculates a fit scorerepresenting the degree of fit between the second dominant cognitivemotivation orientation set and the first dominant cognitive motivationorientation set. Detailed discussion of an exemplary method forcalculating a fit score is provided below in the context of FIG. 12; ingeneral, a fit score may be calculated based on how close the seconddominant cognitive motivation orientation set is to an “ideal” dominantcognitive motivation orientation set given the first dominant cognitivemotivation orientation set. Steps 534 through 572 can be repeated for amodified second text sequence; e.g. one that has been modified based onthe recommendations at step 570. The fit score may provide a finerresolution than the “fits” or “misfits” test at step 564, and thereforecan provide a measurement of whether the modifications have brought thesecond dominant cognitive motivation orientation set closer to matchingthe first dominant cognitive motivation orientation set.

The exemplary method 500 shows, for ease of illustration, a statefulembodiment in which the dominant cognitive motivation orientation setfor the first text sequence may be stored so that it can be used inresponse to a subsequent query. Specifically, steps 502 to 532 may beexecuted in response to a query seeking identification of the dominantcognitive motivation orientation set for the first text sequence, orrecommendations for preparing a meaningful second text sequence thataddresses the first text sequence, or both. The dominant cognitivemotivation orientation set for the first text sequence may be stored ator immediately following step 530 (or at or immediately following thecompletion of all iterations of steps 520 to 526 if steps 528 and 530are bypassed) so that it can be used in response to a subsequent query.As a result, when the method 500 receives query seeking a comparison ofthe dominant cognitive motivation orientation set for the first textsequence to the dominant cognitive motivation orientation set for ameaningful second text sequence that addresses the first text sequenceat step 534, it is only necessary to execute steps 534 to 562 todetermine the dominant cognitive motivation orientation set for thesecond text sequence. The dominant cognitive motivation orientation setfor the second text sequence can then be compared to the stored dominantcognitive motivation orientation set for the first text sequence. Thus,steps 502 to 532 need not be repeated and duplicative processing isavoided.

In an alternative stateless embodiment, the dominant cognitivemotivation orientation set for the first text sequence is not stored foruse in response to a subsequent query. In a stateless embodiment, aquery seeking identification of the dominant cognitive motivationorientation set for the first text sequence, or recommendations forpreparing a meaningful second text sequence that addresses the firsttext sequence, or both, would trigger processing of steps 502 to 532only, after which the method 500 would end. In a stateless embodiment ofthe method 500, a query seeking a comparison of the dominant cognitivemotivation orientation set for the first text sequence to the dominantcognitive motivation orientation set for a meaningful second textsequence that addresses the first text sequence would include both thefirst text sequence and the second text sequence. In response to thisquery, steps 502 to 566 or 570 would be executed, with step 532 beingomitted and the dominant cognitive motivation orientation sets for thefirst text sequence and the second text sequence would be discardedafter step 566 or 570. In a particular embodiment, steps 502 to 530 andsteps 534 to 562 may proceed substantially in parallel. In a mannersimilar to that described above in the context of steps 520 to 528,steps 520 to 526 and 552 to 558 may be omitted where the dominancedetermination is inherent in the ranking carried out at steps 528 and560, and an identification of the ranking may be sent to anothercomputer system for further processing. For example, the values used forthe ranking for each of the first text sequence and the second textsequence may be sent in an ordered list. The first value in the list maycorrespond to the “toward” cognitive motivation orientation for thefirst text sequence and the second value may correspond to the “toward”cognitive motivation orientation for the second text sequence, the thirdvalue in the list may correspond to the “away from” cognitive motivationorientation for the first text sequence and the fourth value in the listmay correspond to the “away from” cognitive motivation orientation forthe second text sequence, and so on. Alternatively, two ordered listsmay be used. A first computer system may execute the method 300, and asecond computer system may execute steps 502 to 528 (possibly omittingsteps 520 to 526) and 534 to 560 (possibly omitting steps 552 to 558)and then return the values resulting from steps 528 and 560 to the firstcomputer system. The first computer system could then carry out steps530 and 532 and 562 to 570.

In some embodiments, the method 500 may incorporate a length test forthe first text sequence (and the second text sequence, whereapplicable), and if the text sequence is too short to analyzeaccurately, the method 500 may return a suitable error message such as“The message is too short to analyze.” In cases where the first textsequence is too short to analyze accurately but the second text sequenceis long enough for accurate analysis, the method 500 may simply analyzethe second text sequence in isolation as if it were a first textsequence.

Similarly, longer text sequences will require more processing resources,so in some embodiments a length limit may be imposed on the textsequences. For example, in an e-mail analysis embodiment using aclient-server architecture as described above and shown in FIG. 4, thee-mail client may send only the first k characters of each textsequence, where k is a positive integer of suitable size and, where thesize k is exceeded, notify the user that only the first k characterswill be analyzed. The method 500 may also carry out an initial lengthtest and reject text sequences exceeding k characters.

In certain embodiments, the cognitive motivation orientations used areselected from the meta programs identified in the field ofneurolinguistics. Table 1 below lists examples of cognitive motivationorientations along with a brief description, language which indicatesthe presence of that cognitive motivation orientation (indicatorlanguage), as well as examples of language to use and to avoid whenseeking to influence a person for whom that cognitive motivationorientation is dominant in a given context:

TABLE 1 OVERVIEW OF COGNITIVE MOTIVATION ORIENTATIONS CognitiveMotivation Indicator Language to Language to Orientation DescriptionLanguage Use Avoid Level Doing or thinking Proactive Likes to jump intoAction verbs, Go for it, do it Think about it action; motivated bypresent tense now, don't wait, for a while, wait, doing hurry up thetime is not ripe, could it happen? Reactive Motivated to wait,Infinitive verbs, Let's consider this, Get on with it, analyze, andconsider passive voice, think it through make it happen, conditionalscarefully, how do it now, don't could it happen?, wait, get it done whenthe time is quickly right Criteria Verbal expression of Exact words andMatch phrases used Use other words values phrases used & repeatedDirection Movement orientation Toward Motivated by goals and Benefits,goals, Rewards, benefits, Avoid, prevent, the benefits of achievingdesires, moving move towards stop them toward Away From Motivated tomove away What they don't Avoid, solve, Goals, rewards, from what theydo not want, not happen, prevent, get away benefits, move want problems,from, don't worry towards, you will undesirable get consequences SourceWhere the decision is made Internal Prefers to judge based on I want, Ineed, What do you You should, you his or her own internal you should, itthink?, it's a choice must, don't do standards; motivated to must, Iknow for you to decide, that, everyone decide for him or herself is thisright?, a will be happy suggestion for you you, many to consider, onlypeople prefer to, you can decide the results will show you ExternalMotivated by outside Is this okay? Everyone will It's up to you,sources, feedback or What should I do? appreciate you, I only you canguidance think you should, decide, what do the results will you think,here is show you some info so that you can decide, do you agree ReasonStraight forward steps or many choices Options Prefers many choices,Variety, ways, Options, choices, The right thing enticed by bending orpossibilities, find ways to do it, find to do, the only breaking rules away around it a way around, way, the way, variety procedure to follow,step by step Procedures Motivated to follow and First, second, Step bystep, Lots of different complete a process; procedure, complete thewhole ways, look at the wants to know the next process thing, it's theright possible options, step and how to do way, the correctpossibilities, something procedure, follow choices, find a these stepsway around Decision Frequency and kind of Factors change neededSameness/ Motivated when things Same, maintain, Keep it the same, New,different, Matching stay the same not changed don't change, the neverbeen done classic version, before, unique, timeless, maintain unusualSameness with Motivated by gradual Better, worse, Improve, gradually,Radical shift, Exception change progress, evolve incremental, stay thesame, upgrade, increase, change, uniquely classic, stable Difference/Motivated by radical Different, new, Unique, be Same, stay theMismatching change, new, different, never been done different, shift,course, slow before, change change, switch, evolution, no revolutionchange, be just like them Scope Size of information General Focuses onthe overview, Generally, the Overview, big Here are all the big picturemain point, picture, mainly, to details, each overview, get to thepoint, the little piece of main idea info, exactly, specificallySpecific Focuses on specific Details, specifics Specific actions, Skipto the end, details one by one, each what's really detail, specifically,important?, exactly details don't matter Attention Attention onnon-verbal Direction behavior or content Self Focuses on the words;Robotic use of Use his or her Fantastic, tends not to notice languageCriteria, you may disaster, notice others' behavior or voice wish toconsider, how it feels, pay tone keep non-verbals to attention to how aminimum he is talking, can you see he is happy? Other Notices andresponds to Voice tone and Match the tone and Speak in a the non-verbalbehavior body language non-verbals robotic way, do of others and usesnon- match the person not show verbals when and situation empathycommunicating Stress Response How a person responds to stressful eventsFeelings Emotional responses to Highly emotional Outrageous, awful,Think about it, normal levels of stress; vocabulary incredibly thelogical thing stays in feelings important, passion to do, work it outstep by step, be rational, use logic Choice Moves in and out of Mix ofemotional Feel and think, Use rational or emotions voluntarily in andrational emotion and logic, emotional stress vocabulary feels right andlanguage only makes good sense, evidence & gut feeling; intuition anddata Thinking Responds rationally in Uses rational Think, work it out,It has to feel stress vocabulary logical, cold right, use your reality,hard facts intuition, gut feel, passion, excitement Style Environmentthat promotes productivity Independent Alone with sole I, sole, myself,By yourself, no one Collaboratively responsibility indicates he/she iswill bother you, work with alone, sole without others, consult atresponsibility interruption, you every step, report will have control,to, we need to go it alone cooperate, us Proximity In control of own I,team, me, them, Your role, their By yourself, do it territory withothers role, your alone, share around responsibility, eachresponsibility team will, you can equally, in direct the team harmonyall together, we Cooperative Together with others in a We, us, our Alltogether, By yourself, do it team, sharing collaboratively, be alone, inresponsibility with us, we, in isolation, no one harmony as a group willbe there, total quiet Sorting Filters/ What a person pays Organizationattention to Person Focuses on people, and Names, mother, Names,experience, Things, results, relationships boss, client, folks people,productivity, relationship, you ideas, methods will feel comfortablethere Thing Focuses on tasks, Things, Things, production, That will feelsystems, ideas, tools, and production, tasks ideas, methods, great,improve material objects results, tasks relationships, people matter,the folks, our buddies Place Focuses on Environment Your place, where Dothis, focus on surroundings, place in a view, where, you are, people,the group place in group surroundings, information environment, theproves, we need place is to take action Information Focuses on data,content, Facts, data, The information is, How they feel, researchinformation what the facts where we are demonstrate, now, let's doresearch indicates, this, people did you know? the matter, step by datawill show step Activity Focuses on actions, what Active verbs What willwe do? The data will is to be done Put that in action, show, peoplelet's do it now, feel, look make it so, you can around, stay still, doit wait for them Rule Structure Behavior rules for self and others My/MyMy rules for me/My You should, if I You know what the You should, rulesfor you; able to tell were you others ought to do, when you're notothers what they expect just tell them what sure, if you don't youthink, you know, it's know the right ambiguous, way, because you maybeyes, are certain that this maybe no needs to be done, let them know My/.My rules for me/ Who cares about Forget about them, They need to beDoesn't pay much them?, they are you are all that catered to, focusattention to others not important matters, do what on others, they youwant, they matter more than won't mind, in you, show them your bestinterest you care, others want No/My Don't know what the Not sure, don'tWhile you may not You know inside rules are for me/I have know, butothers be sure, think about yourself, just rules for others should whatthey need to decide, make it do, even if you so, get on with it, aren'tsure, when come on, aren't you don't know you over this what to do, whatalready? would someone else do? My/Your My rules for me/Your I need,they need, Different strokes Compliance by rules for you; sees both twopoints of for different folks, everyone, same sides; so may be hesitantview to each his own, standards, across to tell others what to do noteveryone is the the board, the same, several acorn doesn't fall pointsof view, far from the tree, many perspectives consistent Convincer Typeof information a Channel person needs to start the process of gettingconvinced See See evidence See, clarity, vision Look at this, see Feelit in your what I mean?, the gut, listen light is on, clarity,carefully, read look here the report, talk it over, say it out loud HearOral presentation or hear Discuss, ask, Ask yourself, It looks great,something listen sounds right, listen read it in the to this, hear allreports, see this, about it, gut feel, feel harmonious good about itRead Read something Read, reports Read about it Let's discuss analyzetext online, read it here, this, what do you the reports are in, see?Gut feel, have you read the look it over, talk book? Reading into aboutit the facts Do Do something Work with it, try Try it on, work Look itover, ask it out, struggle through it, test about it, read the with itdrive it information Convincer How the information Mode previouslygathered is processed to enable a person to become “convinced” ofsomething Number of Certain number of times Numbers of times A couple oftimes, Jump to Examples to be convinced 3 examples, once is conclusions,try not enough, several it for a while, a times over, twice long period,mull now it over for the next week, decide now Automatic Convincedimmediately Know right away, You will know Take lots of time and rarelychange their it's obvious immediately, it will to decide, check minds belove at first site, if you have you can decide doubts, try it a rightaway, don't few times first, wait, get it now look into your doubtsConsistent Never completely You never know, Check it each time You'll beconvinced doubt, possible to make sure, may certain, we know problemsnot quite for sure, you can convincing enough tell right away, a yet,each event is a couple of times little bit different, is enough, do iteach and every for a little while time, you never know Period of TimeNeed a certain duration Day, week, Have it for a while, If you try itonce, to be convinced month, year mull it over for a you'll know, 3couple of weeks, times a winner, how long do you right away, don't need,trial period, a even think about month it, several examples TimeOrientation Past Focused on past events Were, was, had Learn from pastPrepare for the etc been experienced, based future, be here on whathappened, now, plan for last time, in the what is next, past, a year agoleave the past behind, in the now Present In the moment Now, here, inthe What's important The past will present right now, what are show you,think you thinking now, about the future here and now, in consequences,the present, at the think about what moment happened before, yesterday,tomorrow Future Thinking about future future, tomorrow, And in fiveyears Now is all that next year from now, looking matters, the back fromthe present time, future, next year, think about the next month, frompast, we used to, now on past tradition, stick to what/was done

The “Language to Use” and “Language to Avoid” boxes in the table aboveeach provide a short exemplary list which illustrates the types ofphrases which should be used and avoided, respectively, in the secondtext sequence when a particular cognitive motivation orientation is partof the dominant cognitive motivation orientation set expressed in thefirst text sequence. The information in the table above can thus beused, for example, to generate recommendations, either predefined(“canned”) or dynamically, for modifying the second text sequence to fitthe dominant cognitive motivation orientation set for the second textsequence to the dominant cognitive motivation orientation set for thefirst text sequence. One skilled in the art, now informed by the presentdisclosure, can expand upon the exemplary “Language to Use” and“Language to Avoid” with different words and phrases having similarmeanings.

In one particular embodiment, the cognitive motivation orientationswhich the method 100 (as exemplified by the method 500) detects are“toward”, “away from”, “internal”, “external”, “options” and“procedures”. Table 2 below is a correspondence table illustratingexemplary mappings between the dominant cognitive motivation orientationset determined for the first text sequence and the ideal dominantcognitive motivation orientation set for the second text sequence forsuch an embodiment in which m (the number of most dominant cognitivemotivation orientations included in the dominant cognitive motivationorientation set) is equal to 2 at least for the dominant cognitivemotivation orientation for the first text sequence. In the cellsunderneath each cognitive motivation orientation under the heading“Ideal dominant cognitive motivation orientation set for second textsequence”, a “y” means that cognitive motivation orientation must bedominant, an “n” means that cognitive motivation orientation must not bedominant, and an “i” indicates that the ideal dominant cognitivemotivation orientation set for the second text sequence is indifferentto whether that cognitive motivation orientation is dominant ornon-dominant.

TABLE 2 CORESPONDENCE BETWEEN COGNITIVE MOTIVATION ORIENTATION SET FORFIRST TEXT SEQUENCE AND IDEAL COGNITIVE MOTIVATION ORIENTATION SET FORSECOND TEXT SEQUENCE Dominant cognitive Ideal dominant cognitivemotivation motivation orientation set orientation set for second textsequence for first text sequence Away-From Toward External InternalOptions Procedures Away-from Toward y y i i i i Options Away-from y i ii y i Procedures Toward i y i i i y Toward Options i y i i y i OptionsProcedures i i i i y y Procedures Away-from y i i i i y Away-from None yi i i i i Toward None i y i i i i Options None i i i i y i ProceduresNone i i i i i y None None i i y n y i Toward Internal i y y n y iInternal External i i y n y i Away-from Internal y i y n y i InternalOptions i i y n y i Internal Procedures i i y n y y Internal None i i yn y i Toward External i y n y y i External Options i i n y y i ExternalProcedures i i n y y y Away-from External y i n y y i External None i in y y i

It should be noted that while certain rows in the above table may appearto be contradictory, for example, where “toward” and “away from” areidentified as the two most dominant cognitive motivation orientations,this merely indicates that, within that context, the individual whocomposed the relevant text sequence is in the middle of the continuum oris equally motivated by both.

Table 2 above identifies certain exemplary dominant cognitive motivationorientation sets, but is not intended to limit the application of thesystems, methods and computer program products to dominant cognitivemotivation orientation sets formed by a combination of those cognitivemotivation orientations, and the systems, methods and computer programproducts can be extended to encompass a wide variety of cognitivemotivation orientations, including other cognitive motivationorientations listed in Table 1.

The rules shown in the above table can be used to determine, for exampleat step 112 of the method 100 or step 566 of the method 500, whether thedominant cognitive motivation orientation set for the second textsequence fits or misfits the dominant cognitive motivation orientationset for the first text sequence. Where the current dominant cognitivemotivation orientation set for the second text sequence is the idealdominant cognitive motivation orientation set for the second textsequence, the dominant cognitive motivation orientation set for thesecond text sequence fits the dominant cognitive motivation orientationset for the first text sequence. Conversely, where the current dominantcognitive motivation orientation set for the second text sequencediffers from the ideal dominant cognitive motivation orientation set forthe second text sequence, the dominant cognitive motivation orientationset for the second text sequence misfits the dominant cognitivemotivation orientation set for the first text sequence.

The rules shown in the above table can be used to:

-   -   provide recommendations for preparation of a meaningful second        text sequence addressing the first text sequence (e.g. step 106B        of the method 100 or step 532 of the method 500);    -   provide recommendations for modifying the second text sequence        to fit the dominant cognitive motivation orientation set        expressed within the second text sequence to the dominant        cognitive motivation orientation set expressed within the first        text sequence (e.g. step 118 of the method 100, step 570 of the        method 500);    -   automatically select a predetermined response to the first text        sequence from a set of predetermined responses (e.g. step 106C        of the method 100); and/or    -   automatically dynamically generate a response to the first text        sequence (e.g. step 106D of the method 100).

The table above illustrates the following rules for testing whether thedominant cognitive motivation orientation set for the second textsequence fits the dominant cognitive motivation orientation set for thefirst text sequence:

-   -   where the dominant cognitive motivation orientation for the        first text sequence is the away-from cognitive motivation        orientation alone, the dominant cognitive motivation orientation        set for the second text sequence fits the dominant cognitive        motivation orientation set for the first text sequence where the        dominant cognitive motivation orientation set for the second        text sequence includes the away-from cognitive motivation        orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence is the toward cognitive motivation        orientation alone, the dominant cognitive motivation orientation        set for the second text sequence fits the dominant cognitive        motivation orientation set for the first text sequence where the        dominant cognitive motivation orientation set for the second        text sequence includes the toward cognitive motivation        orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence is the options cognitive motivation        orientation alone, the dominant cognitive motivation orientation        set for the second text sequence fits the dominant cognitive        motivation orientation set for the first text sequence where the        dominant cognitive motivation orientation set for the second        text sequence includes the options cognitive motivation        orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence is the procedures cognitive motivation        orientation alone, the dominant cognitive motivation orientation        set for the second text sequence fits the dominant cognitive        motivation orientation set for the first text sequence where the        dominant cognitive motivation orientation set for the second        text sequence includes the procedures cognitive motivation        orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence is the internal cognitive motivation        orientation alone, the dominant cognitive motivation orientation        set for the second text sequence fits the dominant cognitive        motivation orientation set for the first text sequence where the        dominant cognitive motivation orientation set for the second        text sequence includes the external cognitive motivation        orientation and the options cognitive motivation orientation and        excludes the internal cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence is the external cognitive motivation        orientation alone, the dominant cognitive motivation orientation        set for the second text sequence fits the dominant cognitive        motivation orientation set for the first text sequence where the        dominant cognitive motivation orientation set for the second        text sequence includes the internal cognitive motivation        orientation and the options cognitive motivation orientation and        excludes the external cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the away-from cognitive motivation        orientation and the towards cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the away-from cognitive motivation orientation and the towards        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the away-from cognitive motivation        orientation and the options cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the away-from cognitive motivation orientation and the options        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the away-from cognitive motivation        orientation and the procedures cognitive motivation orientation        as the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the away-from cognitive motivation orientation and the        procedures cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the away-from cognitive motivation        orientation and the internal cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the away-from cognitive motivation orientation, the external        cognitive motivation orientation and the options cognitive        motivation orientation and excludes the internal cognitive        motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the away-from cognitive motivation        orientation and the external cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the away-from cognitive motivation orientation, the internal        cognitive motivation orientation and the options cognitive        motivation orientation and excludes the external cognitive        motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the toward cognitive motivation        orientation and the procedures cognitive motivation orientation        as the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the toward cognitive motivation orientation and the procedures        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the toward cognitive motivation        orientation and the options cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the toward cognitive motivation orientation and the options        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the toward cognitive motivation        orientation and the internal cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the toward cognitive motivation orientation, the external        cognitive motivation orientation and the options cognitive        motivation orientation and excludes the internal cognitive        motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the toward cognitive motivation        orientation and the external cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the internal cognitive motivation orientation and the options        cognitive motivation orientation and the towards cognitive        motivation orientation and excludes the external cognitive        motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the options cognitive motivation        orientation and the procedures cognitive motivation orientation        as the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the options cognitive motivation orientation and the procedures        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the options cognitive motivation        orientation and the internal cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the external cognitive motivation orientation and the options        cognitive motivation orientation and excludes the internal        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the internal cognitive motivation        orientation and the external cognitive motivation orientation as        the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the external cognitive motivation orientation and the options        cognitive motivation orientation and excludes the internal        cognitive motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the internal cognitive motivation        orientation and the procedures cognitive motivation orientation        as the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the external cognitive motivation orientation, the options        cognitive motivation orientation and the procedures cognitive        motivation orientation and excludes the internal cognitive        motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence includes the external cognitive motivation        orientation and the procedures cognitive motivation orientation        as the most dominant cognitive motivation orientations, the        dominant cognitive motivation orientation set for the second        text sequence fits the dominant cognitive motivation orientation        set for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the internal cognitive motivation orientation, the options        cognitive motivation orientation and the procedures cognitive        motivation orientation and excludes the external cognitive        motivation orientation;    -   where the dominant cognitive motivation orientation set for the        first text sequence is determined to be null, the dominant        cognitive motivation orientation set for the second text        sequence fits the dominant cognitive motivation orientation set        for the first text sequence where the dominant cognitive        motivation orientation set for the second text sequence includes        the external cognitive motivation orientation and the options        cognitive motivation orientation and excludes the internal        cognitive motivation orientation.

Thus, step 564 may compare the current second dominant cognitivemotivation orientation set to the ideal dominant cognitive motivationorientation set for the second text sequence as set out in Table 2above.

Reference is now made to FIG. 12, which shows an exemplary method 1200for calculating a fit score. In summary, the fit score is the sum ofcertain calculated values derived from the normalized cognitivemotivation orientation weight scores for each cognitive motivationorientation in the second dominant cognitive motivation orientation setthat must be dominant or must be non-dominant in the ideal dominantcognitive motivation orientation set for the second text sequence. Wherea cognitive motivation orientation that must be dominant in the idealdominant cognitive motivation orientation set is dominant in the seconddominant cognitive motivation orientation set, and when a cognitivemotivation orientation that must not be dominant in the ideal dominantcognitive motivation orientation set is non-dominant in the seconddominant cognitive motivation orientation set, a maximum value will becontributed to the fit score. Where a cognitive motivation orientationthat must be dominant in the ideal dominant cognitive motivationorientation set is non-dominant in the second dominant cognitivemotivation orientation set, and when a cognitive motivation orientationthat must not be dominant in the ideal dominant cognitive motivationorientation set is dominant in the second dominant cognitive motivationorientation set, the contribution to the fit score will be based on howfar away the normalized cognitive motivation orientation weight scorefor that cognitive motivation orientation is from being dominant ornon-dominant, respectively. Normalized cognitive motivation orientationweight scores for cognitive motivation orientations to which the idealdominant cognitive motivation orientation set for the second textsequence is indifferent are not included in the calculation.

In a preferred embodiment, the fit score has a maximum value of 100, andeach cognitive motivation orientation that must be dominant or must benon-dominant in the ideal dominant cognitive motivation orientation setfor the second text sequence can contribute a maximum value equal to 100divided by the number of cognitive motivation orientations that must bedominant or must be non-dominant in the ideal dominant cognitivemotivation orientation set. For example, if there are three cognitivemotivation orientations that must be dominant and one cognitivemotivation orientation that must be non-dominant, each cognitivemotivation orientation can contribute a maximum of 25 to the fit score(100/4=25) and if there are two cognitive motivation orientations thatmust be dominant and one cognitive motivation orientation that must benon-dominant, each cognitive motivation orientation can contribute amaximum of 33.33 to the fit score (100/3=33.33). If there is onecognitive motivation orientation that must be dominant and no cognitivemotivation orientations that must not be dominant, that single cognitivemotivation orientation would contribute a maximum of 100 to the fitscore (100/1=100). In the preferred embodiment described in the contextof FIG. 12 and the method 1200 the respective normalized first textsequence cognitive motivation orientation weight scores are mappedaccording to a scaling in which the portion from zero to thecorresponding normalized dominance threshold is mapped to normalizedrange of −100 to zero, so that zero serves as the boundary betweendominant and non-dominant, as described above.

At step 1202, the method 1200 checks whether there are any more dominantcognitive motivation orientations in the ideal dominant cognitivemotivation orientation set. Responsive to a “yes” at step 1202, themethod 1200 proceeds to step 1204 and advances to the next (or first)cognitive motivation orientation that must be dominant in the idealdominant cognitive motivation orientation set for the second textsequence. At step 1206, the method 1200 checks whether that samecognitive motivation orientation is dominant in the second dominantcognitive motivation orientation set. A “yes” determination at step 1206indicates that that cognitive motivation orientation is dominant when itis supposed to be, and the method proceeds to step 1208 and increasesthe fit score by the maximum and then returns to step 1202. Responsiveto a “no” determination at step 1206, indicating that the cognitivemotivation orientation is not dominant when it should be, the method1200 proceeds to step 1210.

At step 1210, the method 1200 compares the magnitude of the scalednormalized cognitive motivation orientation weight score to a limitvalue. Although the boundary between dominant and non-dominant is set atzero for the scaled normalized cognitive motivation orientation weightscores, the magnitude of the distance of a scaled normalized cognitivemotivation orientation weight score from the boundary is relevant. Forexample, scaled normalized cognitive motivation orientation weightscores of −70 and −2 both indicate non-dominance, but the latter is muchcloser to being dominant than the former. A slight improvement (e.g. anincrease of 5) to a scaled normalized cognitive motivation orientationweight score of −2 might make the corresponding cognitive motivationorientation dominant, whereas the same magnitude of improvement to ascaled normalized cognitive motivation orientation weight score of −70would make very little difference. The limit value is selected as themagnitude from the zero boundary where improvements to the scalednormalized cognitive motivation orientation weight score begin to have asignificant effect and therefore should be taken into account incalculating the fit score. In a preferred embodiment, the limit value is30, although other limit values may also be used.

Accordingly, at step 1210, the method 1200 tests whether the scalednormalized cognitive motivation orientation weight score for the currentcognitive motivation orientation is less than −1 multiplied by the limitvalue. Since at step 1210 the method 1200 is handling a case where thecurrent cognitive motivation orientation should be dominant but is not,the scaled normalized cognitive motivation orientation weight score isnegative and the limit value is multiplied by −1 for the comparison. Ifthe scaled normalized cognitive motivation orientation weight score forthe current cognitive motivation orientation is less than −1 multipliedby the limit value (“yes” at step 1210), the method 1200 proceeds tostep 1212 and sets a dominance value used in calculating the fit scoreequal to −1 multiplied by the limit value. If the scaled normalizedcognitive motivation orientation weight score for the current cognitivemotivation orientation is greater than or equal to −1 multiplied by thelimit value (“no” at step 1210) then the dominance value is set equal tothe scaled normalized cognitive motivation orientation weight score forthe current cognitive motivation orientation at step 1214. In otherembodiments, if the dominance value is initialized to the scalednormalized cognitive motivation orientation weight score for the currentcognitive motivation orientation, step 1214 may be omitted.

After either step 1212 or 1214, the method 1200 proceeds to step 1216,where the method 1200 increases the fit score according the formula:maximum*(1−((dominance value*−1)/limit value). Where the dominance valuewas set equal to −1 multiplied by the limit value, the formula reducesto zero; in other cases the amount by which the fit score is increasedwill be positive and will be greater the closer the normalized cognitivemotivation orientation weight score is to zero. After step 1216, themethod 1200 returns to step 1202 to check whether there are any moredominant cognitive motivation orientations in the ideal dominantcognitive motivation orientation set.

Responsive to a “no” at step 1202, indicating that all of the cognitivemotivation scores that must be dominant in the ideal dominant cognitivemotivation orientation set have been considered, the method 1200proceeds to step 1218 to check whether there are any more cognitivemotivation orientations that must be non-dominant in the ideal dominantcognitive motivation orientation set.

Responsive to a “yes” at step 1218, at step 1220 the method 1200advances to the next (or first) cognitive motivation orientation thatmust be non-dominant in the ideal dominant cognitive motivationorientation set for the second text sequence, and then at step 1222 themethod 1200 checks whether that same cognitive motivation orientation isdominant in the second dominant cognitive motivation orientation set. A“no” determination at step 1222 indicates that that cognitive motivationorientation is non-dominant in the second cognitive motivationorientation set as in the ideal dominant cognitive motivationorientation set, so the method 1200 proceeds to step 1224 and increasesthe fit score by the maximum and then returns to step 1218. Responsiveto a “yes” determination at step 1222, indicating that the cognitivemotivation orientation is dominant when it should not be dominant, themethod 1200 proceeds to step 1226.

At step 1226, the method 1200 tests whether the scaled normalizedcognitive motivation orientation weight score for the current cognitivemotivation orientation exceeds the limit value. At step 1226 the method1200 is handling a case where the current cognitive motivationorientation is dominant when it should not be, so the scaled normalizedcognitive motivation orientation weight score is positive. If the scalednormalized cognitive motivation orientation weight score for the currentcognitive motivation orientation exceeds the limit value (“yes” at step1226), the method 1200 proceeds to step 1228 and sets the dominancevalue used in calculating the fit score equal to the limit value. If thescaled normalized cognitive motivation orientation weight score for thecurrent cognitive motivation orientation is less than or equal to thelimit value (“no” at step 1226) then at step 1230 the dominance value isset equal to the scaled normalized cognitive motivation orientationweight score for the current cognitive motivation orientation.

After either step 1228 or 1230, the method 1200 proceeds to step 1232,where the method 1200 increases the fit score according the formula:maximum*(1−(dominance value/limit value). Where the dominance value wasset equal to the limit value, the formula reduces to zero; in othercases the amount by which the fit score is increased will be greater thecloser the normalized cognitive motivation orientation weight score isto zero. After step 1232, the method 1200 returns to step 1218 to checkwhether there are any more cognitive motivation orientations that mustnot be dominant in the ideal dominant cognitive motivation orientationset. Responsive to a “no” determination at step 1218, indicating thatthe method 1200 has considered all cognitive motivation orientationsthat must be dominant or must not be dominant in the ideal cognitivemotivation orientation set (i.e. all non-indifferent cognitivemotivation orientations), the method 1200 ends.

The method 1200 is merely one example of a method for calculating a fitscore; other methods may also be used.

Reference is now made to FIG. 6, which is a flow chart showing anexemplary method 600 for building an analysis database associatingn-grams with cognitive motivation orientations. A database builtaccording to the method 600 shown in FIG. 6 may provide the plurality ofpredetermined indicator n-grams used at steps 508 and 540 of the method500 shown in FIGS. 5A, 5B and 5C.

At step 602, the method 600 receives a training corpus comprising aplurality of training documents. The training documents may consist of avariety of types of document in electronic form, including e-mailmessage bodies, letter contents, text message contents, essays, newsarticles, and so on. For documents that did not originate in electronicform, the documents may be manually retyped, scanned using OCRtechnology or otherwise converted into electronic form. The trainingdocuments may be general in nature, covering a range of subject matter,or may be specialized training documents associated with particularfields. For example, to build an analysis database useful for analyzingcommunications between lawyers, the training documents could come frome-mails, letters and text messages between lawyers, or between lawyersand their clients, legal agreements, or court documents. Similarly,suitably selected training documents may be used to build analysisdatabases for other fields or professions, such as medicine oraccounting, or for particular business areas such as manufacturing,consulting, human resources or marketing.

Each of the training documents comprises a plurality of meaningfullyarranged words. These words may include emoticons and informalabbreviations, such as “l8r” (meaning “later”), “ttyl” (meaning “talk toyou later” and “ROTFL” (meaning “rolling on the floor laughing”) as wellas well-defined dictionary words. In addition, the words in the trainingdocuments may include common grammar and spelling errors. Each of thetraining documents has at least one annotated word sequence of one ormore words therein, and each of these annotated word sequences isannotated with a corresponding word-sequence-level annotationidentifying at least one cognitive motivation orientation associatedwith that annotated word sequence. For example, the word sequence “finda way around it” may be annotated with a word-sequence-level annotationidentifying the “options” cognitive motivation orientation as beingassociated with that word sequence, and the word sequence “what I don'twant to happen is” may be annotated with a word-sequence-levelannotation identifying the “away from” cognitive motivation orientationas being associated with that word sequence. Typically, theword-sequence-level annotations would be based on an identification ofthe association between the relevant word sequence and the cognitivemotivation orientation by a human individual skilled in the field ofneurolinguistics. Examples of such associations can be seen in the“Indicator Language” column in Table 1 above, some of the cells of whichinclude word sequences which indicate, and hence are associated with,the cognitive motivation association for the row in which that cell isfound. Annotation may be carried out, for example, using a modifiedversion of the MMAX2 annotation tool (the current open source release ofMMAX2 is available at http://sourceforge.net/projects/max2/files/).

In a case where a word sequence is associated with more than onecognitive motivation orientation, that word sequence may have a singleannotation identifying each cognitive motivation orientation with whichit is associated, or may have one annotation for each such cognitivemotivation orientation.

At steps 604 through 614, the method 600 generates indicator candidaten-grams.

At step 604, the method 600 advances to the next (or first) trainingdocument, and then advances to the next (or first) unexamined annotatedword sequence in the current training document at step 606.

At step 608, the method 600 extracts n-grams overlapping the currentannotated word sequence. In particular, at step 608, for each positiveinteger n where 0<n≤x and x is a positive integer, the method extractsall n-grams overlapping that annotated word sequence. In a presentlypreferred embodiment, x=3 and at step 608 all unigrams, bigrams andtrigrams overlapping the current annotated word sequence are extracted.For example, if the word sequence “find a way around it” is annotatedwith the “options” cognitive motivation orientation, step 608 wouldextract the following n-grams: “find”, “a”, “way”, “around”, “it”, “finda”, “a way”, “way around”, “around it”, “find a way”, “a way around”,and “way around it”. In alternative embodiments, unigrams and/or othershorter n-grams may be ignored, and step 608 may extract all n-gramsoverlapping the annotated word sequence for all cases where y<n≤x and xand y are positive integers. For example, where y=3 and x=6, step 608would extract 4-grams, 5-grams and 6-grams. After step 608, the method600 then proceeds to step 610, at which each extracted n-gram isassociated with the cognitive motivation orientation(s) associated withthe current annotated word sequence. Continuing with the example of theword sequence “find a way around it”, each of the n-grams “find”, “a”,“way”, “around”, “it”, “find a”, “a way”, “way around”, “around it”,“find a way”, “a way around”, and “way around it” would be associatedwith the “options” cognitive motivation orientation. Suitable methodsfor extracting the n-grams are within the capability of one skilled inthe art, now informed by the present disclosure, and hence are notdescribed further here.

After step 610, the method 600 proceeds to step 612. At step 612, foreach n-gram extracted at step 608 and associated with the cognitivemotivation orientation(s) associated with the current annotated wordsequence at step 610, the method 600 increments a respective counter.The counter records the number of times that the n-gram appears in thetraining corpus in association with the cognitive motivationorientation(s) with which it was associated at step 610. Thus, therewill be one counter for each n-gram-cognitive motivation orientationpair, and the counter will be incremented each time the method 600identifies (at steps 608 to 610) another instance where the n-gramappears in the training corpus in association with that cognitivemotivation orientation. In one implementation, step 612 may comprisefirst checking for an existing counter for a particular n-gram-cognitivemotivation orientation pair. If an existing counter for thatn-gram-cognitive motivation orientation pair is found, the counter wouldbe incremented by 1; if no such counter yet exists, the counter would becreated and initialized at a value of 1. The information contained inthe counters may be utilized later in applying one or more relevancefilters (step 618) and in assigning confidence weights (step 620) asdescribed further below.

While FIG. 6 shows the method 600 first extracting all n-gramsoverlapping the current annotated word sequence (step 608), thenassociating all of the extracted n-grams with the appropriate cognitivemotivation orientation (step 610), and then incrementing the respectivecounters, this is merely illustrative of one exemplary implementation.For example, the method 600 may equivalently be performed with steps608, 610 and 612 executing substantially simultaneously, such as byassociating each extracted n-gram with the cognitive motivationorientation and incrementing the counter immediately followingextraction thereof.

To facilitate extraction of the n-grams (step 608), the trainingdocuments are preferably tokenized into words as the first stage in theannotation process. Thus, in a preferred embodiment, the trainingdocuments are created by annotating token sequences, and annotationswill start and end at token borders.

After each iteration of steps 608, 610 and 612, the method 600 proceedsto step 614 to check whether there are more unexamined annotated wordsequences in the current training document. Responsive to adetermination that there are more unexamined annotated word sequences inthe current training document (“yes” at step 608), the method 600returns to step 606 to advance to the next unexamined annotated wordsequence. If the method 600 determines at step 614 that there are nomore unexamined annotated word sequences in the current trainingdocument, i.e. because all annotated word sequences have been examined(“no” at step 614), the method 600 proceeds to step 616 to check ifthere are more training documents to be handled. If there are additionaltraining documents that have not yet been handled (“yes” at step 616),the method 600 returns to step 604 to advance to the next trainingdocument. If the method 600 determines at step 616 that there are nomore training documents, i.e. all training documents in the trainingcorpus have been handled (“no” at step 616), the method 600 proceeds tostep 618.

Following completion of the last iteration of steps 604 through 616, themethod 600 will have generated a plurality of indicator candidaten-grams from the training corpus. At step 618, the method applies atleast one relevance filter to these indicator candidate n-grams toobtain a set of indicator n-grams. Exemplary relevance filters aredescribed further below. Each of the indicator n-grams resulting fromapplication of the relevance filter(s) at step 618 has as its associatedcognitive motivation orientation the cognitive motivation orientationwith which the corresponding indicator candidate n-gram is mostfrequently associated. At step 620 the method 600 assigns a confidenceweight to each indicator n-gram; an exemplary method for assigning aconfidence weight is described further below.

While FIG. 6 shows the method 600 first obtaining the set of indicatorn-grams (step 618) and then assigning a confidence weight to eachindicator n-gram (step 620), this is merely illustrative of oneexemplary implementation. For example, the method 600 may equivalentlybe performed with steps 618 and 620 executing substantiallysimultaneously, such as by assigning a confidence weight to eachindicator n-gram when it passes the relevance filter.

In a presently preferred embodiment, the confidence weight assigned to agiven indicator candidate n-gram is equal to: (a) the number of timesthat the respective indicator candidate n-gram appears in the trainingcorpus in association with the cognitive motivation orientation withwhich the corresponding indicator candidate n-gram is most frequentlyassociated, divided by (b) the total number of times that the respectiveindicator candidate n-gram appears in the training corpus. The count forcomponent (a) of this calculation may be obtained using the relevantcounter from step 612 above, and the count for component (b) of thiscalculation may be obtained using the counter from step 702 of themethod 700 which, as explained in more detail below, is a particularimplementation of step 618.

At step 622 the method 600 builds a normalized dominance threshold foreach of the cognitive motivation orientations. Details of an exemplaryprocedure for building a normalized dominance threshold are describedfurther below in the context of the method 800 and FIG. 8. After step622, the method 600 ends.

The result of the method 600 is an analysis database associating n-gramswith cognitive motivation orientations, with each n-gram-cognitivemotivation orientation pair having a confidence weight indicating thestrength of that n-gram as an indicator (indicator n-gram) of thepresence of the associated cognitive motivation orientation. Theanalysis database also includes a normalized dominance threshold foreach of the cognitive motivation orientations. By using an analysisdatabase built according to the method 600, a method such as the method100 can perform an objective statistical analysis of a text sequencebased on empirical evidence of the outcome of the application of humanexpertise, as reflected in the training corpus and tuning corpus. Thus,the method 600 effectively transforms a set of annotated documents thatreflect the application of human expertise to specific cases (wordsequences as part of larger documents) into a generalizedcomputer-usable repository of that human knowledge, enabling a computerto directly apply that human knowledge, for example by way of the method100.

The database associating n-grams with cognitive motivation orientationscan be tested by applying the analysis method that uses the database(e.g. the method 500 in FIGS. 5A, 5B and 5C) to a testing corpus of textsequences that are not annotated but for which the dominant cognitivemotivation orientation set is known based on expert human analysis. Theprecision and recall of the system can be calculated. Precision refersto the proportion of the identified dominant patterns that are in factdominant, and recall describes the proportion of the dominant patternsthat are actually retrieved. Precision and recall can be combined into asingle value as a measure of overall system performance.

By adding more training documents/tuning documents and re-executing themethod 600, the analysis database may be incrementally improved. Forexample, a server executing the method 500 may, with user consent and incompliance with all applicable laws including privacy laws, beconfigured to store text sequences, such as e-mail message content.Those stored text sequences can then be annotated so as to becometraining documents and/or tuning documents for use in a subsequentiteration of the method 600.

Reference is now made to FIG. 7, which is a flow chart showing anexemplary method 700 for applying a series of relevance filters to theindicator candidate n-grams to obtain indicator n-grams. Thus, themethod 700 is an exemplary implementation of step 618 of the method 600.The method 700 makes use of the counters for each n-gram-cognitivemotivation orientation pair described above in the context of step 616of the method 600.

At the first iteration of step 702, the method 700 advances to the firstindicator candidate n-gram. At step 704, the method 700 scans thetraining corpus for instances of the current n-gram and counts thoseappearances. This step records the total number of times that thecurrent n-gram appears in the training corpus, regardless of whichcognitive motivation orientation(s) it is associated with by annotation,and also includes instances where the n-gram appears without beingassociated with any cognitive motivation orientation. Implementation ofstep 704 is within the capability of one skilled in the art, nowinformed by the present disclosure, and hence details of the specificprocedure for this step are omitted for brevity. Although counting ofthe total number of appearances of each n-gram may be carried out atother stages of the method 600, the embodiment shown in FIG. 7 resultsin the training corpus being scanned only for n-grams that are indicatorcandidate n-grams. After step 704, the method 700 proceeds to step 706,which applies a first relevance filter.

At step 706, the method 700 tests whether the number of times that therespective indicator candidate n-gram appears in the training corpus inassociation with the cognitive motivation orientation with which thecorresponding indicator candidate n-gram is most frequently associatedis less than a predetermined minimum multiple of a number of times thatthe respective indicator candidate n-gram appears in the training corpusin association with the cognitive motivation orientation with which thecorresponding indicator candidate n-gram is second-most frequentlyassociated. For example, consider a particular n-gram in the trainingcorpus that is most frequently associated in the with the towardcognitive motivation orientation and second most frequently associatedwith the procedures cognitive motivation orientation. If that n-gramappears p times in association with the toward cognitive motivationorientation and q times in association with the procedures cognitivemotivation orientation, where the predetermined minimum multiple is r,step 706 will test whether p>r*q or the mathematical equivalent ofwhether p/q>r. Information about the frequency of associations may beobtained from the counters incremented at step 612 of the method 600. A“yes” determination at step 706 means that the current indicatorcandidate n-gram has failed the first relevance filter, and the method700 returns to step 714 to check whether there are more indicatorcandidate n-grams to examine. Thus, the result of failing the firstrelevance filter (a “yes” determination at step 706) is the currentindicator candidate n-gram will be excluded from the set of indicatorn-grams. A “no” determination at step 706 means that the currentindicator candidate n-gram has passed the first relevance filter, andthe method 700 then proceeds to step 708.

At step 708, the method 700 tests whether the number of times that therespective indicator candidate n-gram appears in the training corpus inassociation with the cognitive motivation orientation with which thecorresponding indicator candidate n-gram is most frequently associatedis less than a predetermined minimum number. A “yes” determination atstep 708 means that the current indicator candidate n-gram has failedthe second relevance filter, so the current indicator candidate n-gramwill be excluded from the set of indicator n-grams and the method 700returns to step 714 to check whether there are more indicator candidaten-grams to examine. A “no” determination at step 708 indicates that thecurrent indicator candidate n-gram has passed the second relevancefilter, and the method 700 then proceeds to step 710.

At step 710, the method 700 tests whether the percentage of appearancesof the respective indicator candidate n-gram in the training corpus inassociation with the cognitive motivation orientation with which thecorresponding indicator candidate n-gram is most frequently associatedis less than a predetermined minimum percentage of the total number ofappearances of the respective indicator candidate n-gram in the trainingcorpus. A “yes” determination at step 710 means that the currentindicator candidate n-gram has failed the third relevance filter, andthe method 700 returns to step 714 to check whether there are moreindicator candidate n-grams to examine. Thus, the result of failing thethird relevance filter (a “yes” determination at step 710) the currentindicator candidate n-gram will be excluded from the set of indicatorn-grams. A “no” determination at step 710 means that the currentindicator candidate n-gram has passed the third and final relevancefilter, and the method 700 then proceeds to step 712. At step 712, thecurrent indicator candidate n-gram is added to the set of indicatorn-grams, and the method 700 returns to step 714 to check whether thereare more indicator candidate n-grams to be filtered.

At step 714, which is reached whenever an indicator candidate n-gramfails a relevance test or passes all three relevance tests, the method700 checks whether there are more indicator candidate n-grams to befiltered. Responsive to a “no” determination, which means that therelevance filter has been applied to all of the indicator candidaten-grams, the method 700 ends (i.e. step 618 would end and the method 600would proceed to step 620). Responsive to a “yes” determination at step714, the method 700 returns to step 702 and advances to the nextindicator candidate n-gram at step 702 and then proceeds to step 704.

Steps 706, 708 and 710 may be carried out in any order. In alternativeembodiments, only one or two of the filters represented by steps 706,708 and 710 may be applied, or different filters may be used.

FIG. 8 is a flow chart showing an exemplary method 800 for building anormalized dominance threshold for a cognitive motivation orientation.The method 800 is an exemplary implementation of step 622 of the method600.

At step 802, the method 800 receives a tuning corpus of tuningdocuments, and at step 804, tokenizes the tuning documents.Alternatively, the tuning documents may be received in tokenized form.

Like the training documents, the tuning documents may consist of avariety of types of document in electronic form, including documentsthat did not originate in electronic form but have been converted toelectronic form, and may be general in nature or specialized to aparticular field. Each tuning document in the tuning corpus comprises aplurality of meaningfully arranged words, which, as with the trainingdocuments, may include not only well-defined dictionary words but alsoemoticons and informal abbreviations.

Each tuning document has a respective document-level annotationidentifying a dominant cognitive motivation orientation set for thattuning document. Similarly to the training documents, the document-levelannotation would be based on an identification by a person skilled inthe field of neurolinguistics of the cognitive motivation orientation(s)that are most dominant in the document. Although it is preferable thatthe training corpus and the tuning corpus be two different groups ofdocuments, it is contemplated that there may be overlap between thetraining corpus and the tuning corpus, and even that the training corpusand the tuning corpus may be identical. Where the training corpus andthe tuning corpus are identical, each document has at least one wordsequence therein that is annotated with a correspondingword-sequence-level annotation identifying at least one cognitivemotivation orientation associated with that annotated word sequence, aswell as an overall document-level annotation identifying a dominantcognitive motivation orientation set for that document. In embodimentswhere the training corpus and the tuning corpus are identical, steps 802and 804 may be omitted since the documents will already have beenreceived at step 602 and will have been tokenized prior to step 612.

Steps 806 through 824 of the method 800 obtain, for each tuningdocument, a document raw confidence weight score for each cognitivemotivation orientation. As explained in greater detail below, successiveiterations of steps 810 through 818 will examine the n-grams in eachtuning document and identify each indicator n-gram appearing in thattuning document. The n-grams that are examined in the tuning documentsare those whose sizes correspond to the sizes of the n-grams extractedat step 612. Thus, in one embodiment, for the same value of x as used instep 612, the successive iterations of steps 810 through 818 willexamine the n-grams in each tuning document for each positive integer nwhere 0<n≤x and x is a positive integer. As noted above, on a presentlypreferred embodiment, x=3 so steps 810 through 818 will examine allunigrams, bigrams and trigrams in each tuning document.

At step 806, the method 800 advances to the next (or first) tuningdocument, and then at step 808 counts the number of n-grams in thecurrent tuning document. Step 808 is shown in its present position inthe method 800 for ease of illustration, and may alternatively becarried out at other stages of the method 800. Following step 808, atstep 810 the method 800 indexes to the next (or first) indicator n-gram,and then at step 812 indexes to the next n-gram in the current tuningdocument. At step 814, the method 800 tests whether the current n-gramin the current tuning document matches the current indicator n-gram.Responsive to a “yes” determination at step 814, at step 816 the method800 increments the respective document raw confidence weight score(s)for the cognitive motivation orientation(s) associated with the currentindicator n-gram. The amount of the increment is equal to the confidenceweight for the indicator n-gram associated with that cognitivemotivation orientation. Thus, each time an indicator n-gram isidentified in a tuning document, that tuning document's raw confidenceweight score for the cognitive motivation orientation associated withthe identified indicator n-gram is incremented by the correspondingconfidence weight. After incrementing the respective document rawconfidence weight score(s) at step 816, the method 800 proceeds to step818 to check whether the end of the current tuning document has beenreached. Responsive to a “no” determination at step 814, indicating thatthe current n-gram in the current tuning document does not match thecurrent indicator n-gram, the method 800 proceeds directly to step 820.

If at step 818 the method 800 determines that the end of the currenttuning document has not yet been reached (“no” at step 818), the method800 returns to step 812 and indexes to the next n-gram in the currenttuning document. If the method 800 determines at step 818 that the endof the current tuning document has been reached (“yes” at step 818), themethod 800 proceeds to step 820, which checks if there are moreindicator n-grams for which the current tuning document is to bechecked. Responsive to a “yes” determination at step 820, the method 800proceeds to step 822 to return to the beginning of the current tuningdocument and then returns to step 810 to index to the next indicatorn-gram. If the method 800 determines at step 820 that there are no moreindicator n-grams for which the current tuning document is to be checked(“no” at step 820), the method 800 proceeds to step 824 to check ifthere are more tuning documents to examine. Responsive to adetermination that there are more tuning documents to examine (“yes” atstep 824), the method 800 returns to step 806 to advance to the nexttuning document. Responsive to a determination that there are no moretuning documents to handle, i.e. all tuning documents have been checkedfor indicator n-grams (“no” at step 824), the method 800 proceeds tostep 826.

Steps 806 through 824 of the method 800 implement a procedure in whichthe tuning documents are sequentially scanned for each indicator n-gramin turn. This is merely one exemplary procedure; other procedures mayequivalently be used. For example, tuning documents could be scanned bysequentially comparing each n-gram in the tuning document to the list ofindicator n-grams.

At step 826, the method 800 normalizes the document raw confidenceweight scores computed by step 16 for each cognitive motivationorientation to obtain, for each tuning document, normalized documentconfidence weight scores for each cognitive motivation orientation. In apresently preferred embodiment, the raw confidence weight scores arenormalized by dividing the raw confidence weight scores by the number oftokens in the respective tuning document.

At step 828, the method 800 selects, for each cognitive motivationorientation, a normalized dominance threshold that minimizes the numberof incorrectly classified tuning documents. A tuning document will beincorrectly classified with respect to a particular cognitive motivationorientation if one of the following conditions is met:

-   -   (a) the normalized document confidence weight score for that        cognitive motivation orientation exceeds the normalized        dominance threshold where that cognitive motivation orientation        is absent from the document-level annotation for that tuning        document; or    -   (b) the normalized document confidence weight score for that        cognitive motivation orientation is less than or equal to the        normalized dominance threshold where that cognitive motivation        orientation is present in the document-level annotation for that        tuning document.

Conceptually, for each cognitive motivation orientation the tuningdocuments are arranged from left to right in order of increasingnormalized document confidence weight score, as shown in the tablebelow, and then a “cut” is applied to divide the table into a left and aright side. A tuning document for which the cognitive motivationorientation being considered is dominant is correctly classified if itis on the right side of the cut and incorrectly classified if it is onthe left side of the cut; a tuning document for which the cognitivemotivation orientation being considered is non-dominant is correctlyclassified if it is on the left side of the cut and incorrectlyclassified if it is on the right side of the cut.

Tuning Document A B C D E F Cognitive Motivation No No Yes Yes No YesOrientation Dominant? Normalized Document 14 20 45 55 80 100 ConfidenceWeight Score

The normalized dominance threshold is assigned a value corresponding tothe “cut” that produces the fewest incorrect classifications. In theexemplary case illustrated by the table above, the normalized dominancethreshold would be assigned a value between 20 and 45, since a “cut”between 20 and 45 produces the fewest errors, specifically a singleerror by incorrectly classifying tuning document “E”. The choice ofvalue between 20 and 45 is arbitrary given the above dataset since anyvalue in that range (greater than 20 and less than 45) will produce thesame single error; in a preferred embodiment the normalized dominancethreshold is assigned a value equal to the midpoint between thenormalized document confidence weight score immediately below the cutand the normalized document confidence weight score immediately abovethe cut. Thus, for the above example, the normalized dominance thresholdwould be assigned a value of 32.5, which could be rounded to 32 or 33.

After step 828, the method 800 ends.

Reference is now made to FIG. 9, which is a flow chart showing anexemplary method 900 for selecting a normalized dominance threshold fora particular cognitive motivation orientation. Thus, step 828 of themethod 800 can be implemented by executing the method 900 for eachcognitive motivation orientation.

At step 902, the method 900 sets an first interim normalized dominancethreshold equal to the lowest normalized document confidence weightscore. For the sample dataset above, the first interim normalizeddominance threshold would be set equal to 14, the normalized documentconfidence weight score from tuning document A. The method 900 thenproceeds to step 904 to calculate the number of classification errorsresulting from the first interim normalized dominance threshold. Aclassification error occurs where, for a particular tuning document, thenormalized document confidence weight score for a cognitive motivationorientation (a) exceeds the interim normalized dominance threshold forthat cognitive motivation orientation but the document-level annotationfor that tuning document does not identify that cognitive motivationorientation as being within the dominant cognitive motivationorientation set for that tuning document; or (b) does not exceed theinterim normalized dominance threshold for that cognitive motivationorientation but the document-level annotation for that tuning documentidentifies that cognitive motivation orientation as being within thedominant cognitive motivation orientation set for that tuning document.Initially, the number of classification errors will be very high. Usingthe sample dataset above, the calculation at step 904 would show twoclassification errors, namely tuning documents B and E.

After step 904, the method 900 proceeds to step 906 and sets a secondinterim normalized dominance threshold equal to the next-highestnormalized document confidence weight score. Using the dataset above, onthe first iteration of step 906 the second interim normalized dominancethreshold would be set equal to 20, which is the normalized documentconfidence weight score for tuning document B. The method then proceedsto step 908 to calculate the number of classification errors resultingfrom the second interim normalized dominance threshold. In this firstiteration, the calculation at step 908 would show only a singleclassification error, namely tuning document E.

The method 900 then proceeds to step 910, which compares the number ofclassification errors resulting from using the first interim normalizeddominance threshold to the number of classification errors resultingfrom using the second interim normalized dominance threshold. If thenumber of classification errors resulting from using the second interimnormalized dominance threshold is less than or equal to the number ofclassification errors resulting from using the first interim normalizeddominance threshold (“yes” at step 910), this indicates that the secondinterim normalized dominance threshold is better than the first interimnormalized dominance threshold, and the method 900 proceeds to step 912.Continuing to use the example dataset above, since using the secondinterim normalized dominance threshold produced only a single errorwhile using the first interim normalized dominance threshold producedtwo errors, the method would proceed to step 912.

At step 912, the method 900 updates the first interim normalizeddominance threshold by setting its value equal to the current value ofthe second interim normalized dominance threshold. Using the sampledata, the first interim normalized dominance threshold would be setequal to 20. The method 900 then returns to step 906 to set a new valueof the second interim normalized dominance threshold equal to thenext-highest normalized document confidence weight score. Thus, thevalue of the second interim normalized dominance threshold would be setequal to 45 based on the sample dataset above. It is not necessary tore-execute step 904 because the number of classification errors for theupdated first interim normalized dominance threshold was alreadycalculated at the previous iteration of step 908.

Steps 906 through 912 will continue to iterate to increase the secondinterim normalized dominance threshold to the next highest normalizeddocument confidence weight score as long as each new second interimnormalized dominance threshold produces fewer classification errors thanthat which preceded it. As the second interim normalized dominancethreshold continues to increase, it will eventually cease to producefewer classification errors than the one that preceded it. This willresult in a “no” determination at step 910, indicating that the currentsecond interim normalized dominance threshold produces no fewerclassification errors than the previous second interim normalizeddominance threshold, whose value is now reflected in the first interimnormalized dominance threshold. This means that further increases in thesecond interim normalized dominance threshold will not produce anyimprovements in the number of classification errors, and the number ofclassification errors resulting from the value of the first interimnormalized dominance threshold is at least as good as, if not betterthan the number of classification errors resulting from the value of thesecond interim normalized dominance threshold. For example, on the nextiteration of steps 906 through 912 based on the sample dataset above, itis determined that the second interim normalized dominance threshold of45 produces two classification errors, namely tuning documents C and E,which is more than the single classification error produced by the firstinterim normalized dominance threshold of 20. This would produce a “no”at step 910.

Responsive to a “no” at step 910, indicating that further increases inthe second interim normalized dominance threshold will not produce anyimprovements in the number of classification errors, the method 900proceeds to step 914 to set the value of the final normalized dominancethreshold for the cognitive motivation orientation equal to an arbitraryvalue between the first interim normalized dominance threshold and thesecond interim normalized dominance threshold, such as the midpoint.

The exemplary systems and methods described herein are not limited tothe English language, and may be extended, with suitable modification,to other languages. By providing training and tuning documents in a newlanguage, the method 600 can be used to build an analysis databaseassociating n-grams in that language with cognitive motivationorientations, thereby enabling the methods 100, 200 and 300 to beapplied, for example by implementation of the method 500, to that newlanguage. For strongly inflecting languages such as German, a stemmingprocess (computation of word stems based on inflected word forms) can beused to normalize words to their stem, which is expected to lead to morereliable n-gram counts. Other linguistic tools may also be used. Animplementation of the methods 100, 200 and 300 may be enabled to handlemultiple languages through the use of language recognition software toidentify whether any special language handling is to be applied, such asstemming, and to select the analysis database for that language.

Aspects of the present technology have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according to variousembodiments. In this regard, the flowchart and block diagrams in theFigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present technology. Forinstance, each block in the flowchart or block diagrams may represent amodule, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Some specific examples of the foregoing havebeen noted above but these noted examples are not necessarily the onlysuch examples. It will also be noted that each block of the blockdiagrams and/or flowchart illustration, and combinations of blocks inthe block diagrams and/or flowchart illustration, can be implemented byspecial purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

It also will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

An illustrative computer system in respect of which the methods hereindescribed may be implemented is presented as a block diagram in FIG. 10.The illustrative computer system is denoted generally by referencenumeral 1000 and includes a display 1002, input devices in the form ofkeyboard 1004A and pointing device 1004B, computer 1006 and externaldevices 1008. While pointing device 1004B is depicted as a mouse, itwill be appreciated that other types of pointing device may also beused.

The computer 1006 may contain one or more processors or microprocessors,such as a central processing unit (CPU) 1010. The CPU 1010 performsarithmetic calculations and control functions to execute software storedin an internal memory 1012, preferably random access memory (RAM) and/orread only memory (ROM), and possibly additional memory 1014. Theadditional memory 1014 may include, for example, mass memory storage,hard disk drives, optical disk drives (including CD and DVD drives),magnetic disk drives, magnetic tape drives (including LTO, DLT, DAT andDCC), flash drives, program cartridges and cartridge interfaces such asthose found in video game devices, removable memory chips such as EPROMor PROM, emerging storage media, such as holographic storage, or similarstorage media as known in the art. This additional memory 1014 may bephysically internal to the computer 1006, or external as shown in FIG.10, or both.

The computer system 1000 may also include other similar means forallowing computer programs or other instructions to be loaded. Suchmeans can include, for example, a communications interface 1016 whichallows software and data to be transferred between the computer system1000 and external systems and networks. Examples of communicationsinterface 1016 can include a modem, a network interface such as anEthernet card, a wireless communication interface, or a serial orparallel communications port. Software and data transferred viacommunications interface 1016 are in the form of signals which can beelectronic, acoustic, electromagnetic, optical or other signals capableof being received by communications interface 1016. Multiple interfaces,of course, can be provided on a single computer system 1000.

Input and output to and from the computer 1006 is administered by theinput/output (I/O) interface 1018. This I/O interface 1018 administerscontrol of the display 1002, keyboard 1004A, external devices 1008 andother such components of the computer system 1000. The computer 1006also includes a graphical processing unit (GPU) 1020. The latter mayalso be used for computational purposes as an adjunct to, or instead of,the (CPU) 1010, for mathematical calculations.

The various components of the computer system 1000 are coupled to oneanother either directly or by coupling to suitable buses.

FIG. 11 shows an exemplary networked mobile wireless telecommunicationcomputing device in the form of a smartphone 1100. The smartphone 1100includes a display 1102, an input device in the form of keyboard 1104and an onboard computer system 1106. The display 1102 may be atouchscreen display and thereby serve as an additional input device, oras an alternative to the keyboard 1104. The onboard computer system 1106comprises a central processing unit (CPU) 1110 having one or moreprocessors or microprocessors for performing arithmetic calculations andcontrol functions to execute software stored in an internal memory 1112,preferably random access memory (RAM) and/or read only memory (ROM) iscoupled to additional memory 1114 which will typically comprise flashmemory, which may be integrated into the smartphone 1100 or may comprisea removable flash card, or both. The smartphone 1100 also includes acommunications interface 1116 which allows software and data to betransferred between the smartphone 1100 and external systems andnetworks. The communications interface 1116 is coupled to one or morewireless communication modules 1124, which will typically comprise awireless radio for connecting to one or more of a cellular network, awireless digital network or a Wi-Fi network. The communicationsinterface 1116 will also typically enable a wired connection of thesmartphone 1100 to an external computer system. A microphone 1126 andspeaker 1128 are coupled to the onboard computer system 1106 to supportthe telephone functions managed by the onboard computer system 1106, andGPS receiver hardware 1122 may also be coupled to the communicationsinterface 1116 to support navigation operations by the onboard computersystem 1106. Input and output to and from the onboard computer system1106 is administered by the input/output (I/O) interface 1118, whichadministers control of the display 1102, keyboard 1104, microphone 1126and speaker 1128. The onboard computer system 1106 may also include aseparate graphical processing unit (GPU) 1120. The various componentsare coupled to one another either directly or by coupling to suitablebuses.

The term “computer system”, as used herein, is not limited to anyparticular type of computer system and encompasses servers, desktopcomputers, laptop computers, networked mobile wireless telecommunicationcomputing devices such as smartphones, tablet computers, as well asother types of computer systems.

As will be appreciated by one skilled in the art, aspects of thetechnology described herein may be embodied as a system, method orcomputer program product. Accordingly, aspects of the technologydescribed herein may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Furthermore, aspects of the presently describedtechnology may take the form of a computer program product embodied inone or more computer readable medium(s) carrying computer readableprogram code.

Where aspects of the technology described herein are implemented as acomputer program product, any combination of one or more computerreadable medium(s) may be utilized. The computer readable medium may bea computer readable signal medium or a computer readable storage medium.A computer readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device. Thus, computerreadable program code for implementing aspects of the technologydescribed herein may be contained or stored in the memory 1112 of theonboard computer system 1106 of the smartphone 1100 or the memory 1012of the computer 1006, or on a computer usable or computer readablemedium external to the onboard computer system 1106 of the smartphone1100 or the computer 1006, or on any combination thereof.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radiofrequency, and the like, or anysuitable combination of the foregoing. Computer program code forcarrying out operations for aspects of the presently describedtechnology may be written in any combination of one or more programminglanguages, including an object oriented programming language andconventional procedural programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider).

Finally, the terminology used herein is for the purpose of describingparticular embodiments only and is not intended to be limiting. As usedherein, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope of theclaims. The embodiment was chosen and described in order to best explainthe principles of the technology and the practical application, and toenable others of ordinary skill in the art to understand the technologyfor various embodiments with various modifications as are suited to theparticular use contemplated.

One or more currently preferred embodiments have been described by wayof example. It will be apparent to persons skilled in the art that anumber of variations and modifications can be made without departingfrom the scope of the claims.

What is claimed is:
 1. A computer-implemented method for building ananalysis database associating each of a plurality of n-grams withcorresponding respective cognitive motivation orientations, comprising:receiving a training corpus of training documents in electronic form;each training document comprising a plurality of meaningfully arrangedwords; each training document having at least one annotated wordsequence therein; wherein within each training document, each particularannotated word sequence is annotated with a correspondingword-sequence-level annotation identifying at least one cognitivemotivation orientation that is associated with that particular annotatedword sequence; for each training document: for each annotated wordsequence in that particular training document: extracting n-gramsoverlapping that particular annotated word sequence; and associatingeach extracted n-gram with the at least one cognitive motivationorientation associated with that particular annotated word sequence;generating a set of indicator candidate n-grams wherein: each indicatorcandidate n-gram represents all instances of a particular n-gram in thetraining corpus for which at least one instance of that particularn-gram was extracted from any annotated word sequence in any trainingdocument; each indicator candidate n-gram being associated with everycognitive motivation orientation that is associated with at least oneinstance of the particular n-gram represented by that particularindicator candidate n-gram; applying at least one relevance filter toeach indicator candidate n-gram in the set of indicator candidaten-grams to obtain a set of indicator n-grams, wherein: the set ofindicator n-grams is a subset of the set of indicator candidate n-grams,so that each indicator n-gram corresponds to only one indicatorcandidate n-gram and thereby each indicator n-gram represents allinstances of a corresponding particular n-gram in the training corpusfor which at least one instance of that particular n-gram was extractedfrom any annotated word sequence in any training document; eachindicator n-gram is associated with only a single cognitive motivationorientation; and each indicator n-gram has, as its associated singlecognitive motivation orientation, that single cognitive motivationorientation with which the instances of the particular n-gramrepresented by that particular indicator n-gram are most frequentlyassociated; permitting the analysis database to support enhancedcomputer-implemented neurolinguistic analysis of a text sequence toidentify cognitive motivation orientations expressed within the textsequence quickly, objectively and consistently.
 2. The method of claim1, wherein applying at least one relevance filter to each indicatorcandidate n-gram in the set of indicator candidate n-grams to obtain aset of indicator n-grams comprises: excluding from the set of indicatorn-grams those indicator candidate n-grams for which there is no singlecognitive motivation orientation with which instances of the particularn-gram represented by that particular indicator candidate n-gram aremost frequently associated in the training corpus; and further comprisesat least one of: (a) excluding from the set of indicator n-grams thoseindicator candidate n-grams for which a predetermined minimum multiplecondition is not satisfied, wherein the predetermined minimum multipleis condition satisfied in respect of a particular indictor candidaten-gram only if: (A) there is a single cognitive motivation orientationwith which instances of the particular n-gram represented by thatparticular indicator candidate n-gram are most frequently associated inthe training corpus, wherein instances of the particular n-gramrepresented by that particular indicator candidate n-gram appear p timesin the training corpus in association with the single cognitivemotivation orientation with which instances of the particular n-gramrepresented by that particular indicator candidate n-gram are mostfrequently associated in the training corpus; (B) there is at least onecognitive motivation orientation with which instances of the particularn-gram represented by that particular indicator candidate n-gram aresecond-most frequently associated in the training corpus, whereininstances of the particular n-gram represented by that particularindicator candidate n-gram appear q times in the training corpus inassociation with the at least one cognitive motivation orientation withwhich instances of the particular n-gram represented by that particularindicator candidate n-gram are second-most frequently associated in thetraining corpus; and (C) p exceeds the value resulting from multiplyingq by a predetermined multiplier value r; (b) excluding from the set ofindicator n-grams those indicator candidate n-grams for which p is lessthan a predetermined minimum number; and (c) excluding from the set ofindicator n-grams those indicator candidate n-grams for which acalculated percentage, the calculated percentage being the valueobtained by: dividing p by a total number of appearances in the trainingcorpus of instances of the particular n-gram represented by theparticular indicator candidate n-gram; and multiplying the resultingquotient by
 100. 3. The method of claim 1, further comprising assigninga confidence weight to each indicator n-gram.
 4. The method of claim 3,wherein: assigning a confidence weight to each indicator n-gramcomprises assigning to each indicator n-gram a confidence weight equalto p divided by a total number of appearances in the training corpus ofinstances of the particular n-gram represented by that particularindicator n-gram; where instances of the particular n-gram representedby that particular indicator n-gram appear p times in the trainingcorpus in association with the single cognitive motivation orientationwith which instances of the particular n-gram represented by thatparticular indicator n-gram are most frequently associated in thetraining corpus.
 5. The method of claim 3, further comprising building arespective normalized dominance threshold associated with each of thecognitive motivation orientations.
 6. The method of claim 5, whereinbuilding a respective normalized dominance threshold associated with foreach of the cognitive motivation orientations comprises: receiving atuning corpus of tuning documents; each tuning document comprising aplurality of meaningfully arranged words; each tuning document beingannotated with a respective document-level annotation, wherein therespective document-level annotation identifies a dominant cognitivemotivation orientation set assigned to the particular tuning documentannotated by that particular document-level annotation; obtaining, foreach tuning document, respective document raw confidence weight scoresassociated with every cognitive motivation orientation expressed in thatparticular tuning document by, for each tuning document: for eachindicator n-gram: identifying each instance of a matching n-gramappearing in that particular tuning document, wherein the matchingn-gram matches the particular indicator n-gram; and for each instance ofa matching n-gram, incrementing a document raw confidence weight scoreassociated with the cognitive motivation orientation that is associatedwith that particular indicator n-gram matched by the matching n-gram,wherein the incrementing is according to the confidence weight assignedto that particular n-gram; for each tuning document, normalizing thedocument raw confidence weight scores for each cognitive motivationorientation expressed in that particular tuning document by: dividingthe raw confidence weight score associated with each cognitivemotivation orientation expressed in that particular tuning document by anumber of tokens contained in that particular tuning document to assignnormalized document confidence weight scores to each cognitivemotivation orientation expressed in that particular tuning document; andselecting, for each cognitive motivation orientation, a normalizeddominance threshold, wherein the normalized dominance thresholdminimizes a number of incorrectly classified tuning documents, wherein:incorrect classification of a given tuning document with respect to aparticular cognitive motivation orientation is a condition selected fromthe group consisting of: (a) the normalized document confidence weightscore associated with that cognitive motivation orientation with respectto that particular tuning document exceeds the normalized dominancethreshold but that cognitive motivation orientation is absent from thedocument-level annotation annotating that particular tuning document;and (b) the normalized document confidence weight score associated withthat cognitive motivation orientation with respect to that particulartuning document is less than or equal to the normalized dominancethreshold but that cognitive motivation orientation is present in thedocument-level annotation annotating that particular tuning document. 7.A database-building data processing system configured for building ananalysis database associating each of a plurality of n-grams withcorresponding respective cognitive motivation orientations, the systemcomprising: a host computer with memory and at least one processorcoupled to the memory; and a database-building module, thedatabase-building module comprising program code that, when executed inthe memory of the host computer: receives a training corpus of trainingdocuments, wherein: each training document comprises a plurality ofmeaningfully arranged words in electronic form; each training documenthas at least one annotated word sequence therein; wherein within eachtraining document, each particular annotated word sequence is annotatedwith a corresponding word-sequence-level annotation identifying at leastone cognitive motivation orientation that is associated with thatparticular annotated word sequence; for each training document: for eachannotated word sequence in that particular training document: extractsn-grams overlapping that particular annotated word sequence; andassociates each extracted n-gram with the at least one cognitivemotivation orientation associated with that particular annotated wordsequence; generates a set of indicator candidate n-grams wherein: eachindicator candidate n-gram represents all instances of a particularn-gram in the training corpus for which at least one instance of thatparticular n-gram was extracted from any annotated word sequence in anytraining document; each indicator candidate n-gram being associated withevery cognitive motivation orientation that is associated with at leastone instance of the particular n-gram represented by that particularindicator candidate n-gram; applies at least one relevance filter toeach indicator candidate n-gram in the set of indicator candidaten-grams to obtain a set of indicator n-grams, wherein; the set ofindicator n-grams is a subset of the set of indicator candidate n-grams,so that each indicator n-gram corresponds to only one indicatorcandidate n-gram and thereby each indicator n-gram represents allinstances of a corresponding particular n-gram in the training corpusfor which at least one instance of that particular n-gram was extractedfrom any annotated word sequence in any training document; and eachindicator n-gram is associated with only a single cognitive motivationorientation; wherein each indicator n-gram has, as its associated singlecognitive motivation orientation, that single cognitive motivationorientation with which the instances of the particular n-gramrepresented by that particular indicator n-gram are most frequentlyassociated; permitting the analysis database to support enhancedcomputer-implemented neurolinguistic analysis of a text sequence toidentify cognitive motivation orientations expressed within the textsequence quickly, objectively and consistently.
 8. The data processingsystem of claim 7, wherein the program code that, when executed in thememory of the host computer, applies at least one relevance filter toeach indicator candidate n-gram in the set of indicator candidaten-grams to obtain a set of indicator n-grams comprises program codethat, when executed in the memory of the host computer: excludes fromthe set of indicator n-grams those indicator candidate n-grams for whichthere is no single cognitive motivation orientation with which instancesof the particular n-gram represented by that particular indicatorcandidate n-gram are most frequently associated in the training corpus;and further does at least one of: (a) excluding from the set ofindicator n-grams those indicator candidate n-grams for which apredetermined minimum multiple condition is not satisfied, wherein thepredetermined minimum multiple condition is satisfied in respect of aparticular indictor candidate n-gram only if: (A) there is a singlecognitive motivation orientation with which instances of the particularn-gram represented by that particular indicator candidate n-gram aremost frequently associated in the training corpus, wherein instances ofthe particular n-gram represented by that particular indicator candidaten-gram appear p times in the training corpus in association with thesingle cognitive motivation orientation with which instances of theparticular n-gram represented by that particular indicator candidaten-gram are most frequently associated in the training corpus; (B) thereis at least one cognitive motivation orientation with which instances ofthe particular n-gram represented by that particular indicator candidaten-gram are second-most frequently associated in the training corpus,wherein instances of the particular n-gram represented by thatparticular indicator candidate n-gram appear q times in the trainingcorpus in association with the at least one cognitive motivationorientation with which instances of the particular n-gram represented bythat particular indicator candidate n-gram are second-most frequentlyassociated in the training corpus; and (C) p exceeds the value resultingfrom multiplying q by a predetermined multiplier value r; (b) excludingfrom the set of indicator n-grams those indicator candidate n-grams forwhich p is less than a predetermined minimum number; and (c) excludingfrom the set of indicator n-grams those indicator candidate n-grams forwhich a calculated percentage is less than a predetermined minimumpercentage, the calculated percentage being the value obtained by:dividing p by a total number of appearances in the training corpus ofinstances of the particular n-gram represented by that particularindicator candidate n-gram; and multiplying the resulting quotient by100.
 9. The data processing system of claim 7, wherein thedatabase-building module further comprises program code that, whenexecuted in the memory of the host computer, assigns a confidence weightto each indicator n-gram.
 10. The data processing system of claim 9,wherein the program code that, when executed in the memory of the hostcomputer, assigns a confidence weight to each indicator n-gram comprisesprogram code that, when executed in the memory of the host computer,assigns a confidence weight to each indicator n-gram wherein for eachindicator n-gram, the respective confidence weight is equal to p dividedby a total number of appearances in the training corpus of instances ofthe particular n-gram represented by that particular indicator n-gram;where instances of the particular n-gram represented by that particularindicator n-gram appear p times in the training corpus in associationwith the single cognitive motivation orientation with which instances ofthe particular n-gram represented by that particular indicator n-gramare most frequently associated in the training corpus.
 11. The dataprocessing system of claim 9, wherein the database-building modulefurther comprises program code that, when executed in the memory of thehost computer, builds a respective normalized dominance thresholdassociated with each of the cognitive motivation orientations.
 12. Thedata processing system of claim 11, wherein the program code that, whenexecuted in the memory of the host computer, builds a respectivenormalized dominance threshold associated with each of the cognitivemotivation orientations comprises program code that, when executed inthe memory of the host computer: receives a tuning corpus of tuningdocuments, wherein: each tuning document comprises a plurality ofmeaningfully arranged words; each tuning document being annotated with arespective document-level annotation, wherein the respectivedocument-level annotation identifies a dominant cognitive motivationorientation set assigned to the particular tuning document annotated bythat particular document-level annotation; obtains, for each tuningdocument, document raw confidence weight scores associated with everycognitive motivation orientation expressed in that particular tuningdocument by, for each tuning document: for each indicator n-gram:identifying each instance of a matching n-gram appearing in thatparticular tuning document, wherein the matching n-gram matches theparticular indicator n-gram; and for each instance of a matching n-gram,incrementing a document raw confidence weight score associated with thecognitive motivation orientation that is associated with that particularindicator n-gram matched by the matching n-gram, wherein theincrementing is according to the confidence weight assigned to thatparticular n-gram; for each tuning document, normalizes the document rawconfidence weight scores for each cognitive motivation orientationexpressed in that particular tuning document by: dividing the rawconfidence weight scores associated with each cognitive motivationorientation expressed in that particular tuning document by a number oftokens contained in that particular tuning document to assign normalizeddocument confidence weight scores to each cognitive motivationorientation expressed in that particular tuning document; and selects,for each cognitive motivation orientation, a normalized dominancethreshold, wherein the normalized dominance threshold minimizes a numberof incorrectly classified tuning documents, wherein: incorrectclassification of a given tuning document with respect to a particularcognitive motivation orientation is a condition selected from the groupconsisting of: (a) the normalized document confidence weight scoreassociated with that cognitive motivation orientation, with respect tothat particular tuning document, exceeds the normalized dominancethreshold but that cognitive motivation orientation is absent from thedocument-level annotation annotating that particular tuning document;and (b) the normalized document confidence weight score associated withthat cognitive motivation orientation, with respect to that particulartuning document, is less than or equal to the normalized dominancethreshold but that cognitive motivation orientation is present in thedocument-level annotation annotating that particular tuning document.13. A computer program product for building an analysis databaseassociating each of a plurality of n-grams with corresponding respectivecognitive motivation orientations, the computer program productcomprising: a non-transitory computer readable storage medium havingcomputer readable program code embodied therewith, the computer readableprogram code comprising: computer readable program code adapted to, whenexecuted by a computer, cause the computer to receive a training corpusof training documents, wherein: each training document comprises aplurality of meaningfully arranged words in electronic form; eachtraining document has at least one annotated word sequence therein;wherein, within each training document, each particular annotated wordsequence is annotated with a corresponding word-sequence-levelannotation identifying at least one cognitive motivation orientationthat is associated with that particular annotated word sequence;computer readable program code adapted to, when executed by a computer,cause the computer to, for each training document: for each annotatedword sequence in that particular training document: extract n-gramsoverlapping that particular annotated word sequence; and associate eachextracted n-gram with the at least one cognitive motivation orientationassociated with that particular annotated word sequence; computerreadable program code adapted to, when executed by a computer, cause thecomputer to generate indicator candidate n-grams wherein: each indicatorcandidate n-gram represents all instances of a particular n-gram in thetraining corpus for which at least one instance of that particularn-gram was extracted from any annotated word sequence in any trainingdocument; each indicator candidate n-gram being associated with everycognitive motivation orientation that is associated with at least oneinstance of the particular n-gram represented by that particularindicator candidate n-gram; computer readable program code adapted to,when executed by a computer, cause the computer to apply at least onerelevance filter to each indicator candidate n-grams in the set ofindicator candidate n-grams to obtain a set of indicator n-grams,wherein; the set of indicator n-grams is a subset of the set ofindicator candidate n-grams, so that each indicator n-gram correspondsto only one indicator candidate n-gram and thereby each indicator n-gramrepresents all instances of a corresponding particular n-gram in thetraining corpus for which at least one instance of that particularn-gram was extracted from any annotated word sequence in any trainingdocument; and each indicator n-gram is associated with only a singlecognitive motivation orientation; wherein each indicator n-gram has, asits associated single cognitive motivation orientation, that singlecognitive motivation orientation with which the instances of theparticular n-gram represented by that particular indicator n-gram aremost frequently associated; permitting the analysis database to supportenhanced computer-implemented neurolinguistic analysis of a textsequence to identify cognitive motivation orientations expressed withinthe text sequence quickly, objectively and consistently.
 14. Thecomputer program product of claim 13, wherein the computer readableprogram code adapted to, when executed by a computer, cause the computerto apply at least one relevance filter to each indicator candidaten-gram in the set of indicator candidate n-grams to obtain a set ofindicator n-grams comprises computer readable program code adapted to,when executed by a computer, cause the computer to: exclude from the setof indicator n-grams those indicator candidate n-grams for which thereis no single cognitive motivation orientation with which instances ofthe particular n-gram represented by that particular indicator candidaten-gram are most frequently associated in the training corpus; andfurther comprises computer readable program code adapted to, whenexecuted by a computer, cause the computer to do at least one of: (a)excluding from the set of indicator n-grams those indicator candidaten-grams for which a predetermined minimum multiple condition is notsatisfied, wherein the predetermined minimum multiple condition issatisfied in respect of a particular indictor candidate n-gram only if:(A) there is a single cognitive motivation orientation with whichinstances of the particular n-gram represented by that particularindicator candidate n-gram are most frequently associated in thetraining corpus, wherein instances of the particular n-gram representedby that particular indicator candidate n-gram appear p times in thetraining corpus in association with the single cognitive motivationorientation with which instances of the particular n-gram represented bythat particular indicator candidate n-gram are most frequentlyassociated in the training corpus; (B) there is at least one cognitivemotivation orientation with which instances of the particular n-gramrepresented by that particular indicator candidate n-gram aresecond-most frequently associated in the training corpus, whereininstances of the particular n-gram represented by that particularindicator candidate n-gram appear q times in the training corpus inassociation with the at least one cognitive motivation orientation withwhich instances of the particular n-gram represented by that particularindicator candidate n-gram are second-most frequently associated in thetraining corpus; and (C) p exceeds the value resulting from multiplyingq by a predetermined multiplier value r; (b) excluding from the set ofindicator n-grams those indicator candidate n-grams for which p is lessthan a predetermined minimum number; and (c) excluding from the set ofindicator n-grams those indicator candidate n-grams for which acalculated percentage is less than a predetermined minimum percentage,the calculated percentage being the value obtained by: dividing p by atotal number of appearances in the training corpus of instances of theparticular n-gram represented by that indicator candidate n-gram; andmultiplying the resulting quotient by
 100. 15. The computer programproduct of claim 13, further comprising computer readable program codeadapted to, when executed by a computer, cause the computer to assign aconfidence weight to each indicator n-gram.
 16. The computer programproduct of claim 15, wherein the computer readable program code adaptedto, when executed by a computer, cause the computer to assign aconfidence weight to each indicator n-gram comprises computer readableprogram code adapted to, when executed by a computer, cause the computerto assign to each indicator n-gram a confidence weight equal to pdivided by a total number of appearances in the training corpus ofinstances of the particular n-gram represented by that particularindicator n-gram; where instances of the particular n-gram representedby that particular indicator n-gram appear p times in the trainingcorpus in association with the single cognitive motivation orientationwith which instances of the particular n-gram represented by thatparticular indicator n-gram are most frequently associated in thetraining corpus.
 17. The computer program product of claim 15, furthercomprising computer readable program code adapted to, when executed by acomputer, cause the computer to build a respective normalized dominancethreshold associated with each of the cognitive motivation orientations.18. The computer program product of claim 17, wherein the computerreadable program code adapted to, when executed by a computer, cause thecomputer to build a respective normalized dominance threshold associatedwith each of the cognitive motivation orientations comprises: computerreadable program code adapted to, when executed by a computer, cause thecomputer to receive a tuning corpus of tuning documents, wherein: eachtuning document comprises a plurality of meaningfully arranged words;each tuning document being annotated with a respective document-levelannotation, wherein the respective document-level annotation identifiesa dominant cognitive motivation orientation set assigned to theparticular tuning document annotated by that particular document-levelannotation; computer readable program code adapted to, when executed bya computer, cause the computer to obtain, for each tuning document,document raw confidence weight scores associated with every cognitivemotivation orientation expressed in that particular tuning document by,for each tuning document: for each indicator n-gram: identifying eachinstance of a matching n-gram appearing in that particular tuningdocument, wherein the matching n-gram matches the particular indicatorn-gram; and for each instance of a matching n-gram, incrementing adocument raw confidence weight score associated with the cognitivemotivation orientation that is associated with that particular indicatorn-gram matched by the matching n-gram, wherein the incrementing isaccording to the confidence weight assigned to that particular n-gram;computer readable program code adapted to, when executed by a computer,cause the computer to, for each tuning document, normalize the documentraw confidence weight scores associated with each cognitive motivationorientation expressed in that particular tuning document by: dividingthe raw confidence weight scores associated with each cognitivemotivation orientation by a number of tokens in that particular tuningdocument to assign normalized document confidence weight scores to eachcognitive motivation orientation expressed in that particular tuningdocument; and computer readable program code adapted to, when executedby a computer, cause the computer to select, for each cognitivemotivation orientation, a normalized dominance threshold, wherein thenormalized dominance threshold minimizes a number of incorrectlyclassified tuning documents, wherein: incorrect classification of agiven tuning document with respect to a particular cognitive motivationorientation is a condition selected from the group consisting of: (a)the normalized document confidence weight score for that cognitivemotivation orientation with respect to that particular tuning document,exceeds the normalized dominance threshold but that cognitive motivationorientation is absent from the document-level annotation annotating thatparticular tuning document; and (b) the normalized document confidenceweight score associated with that cognitive motivation orientation withrespect to that particular tuning document is less than or equal to thenormalized dominance threshold but that cognitive motivation orientationis present in the document-level annotation annotating that particulartuning document.